STATISTICS ........................................................................................................................................................... 6
Q1. WHAT IS THE CENTRAL LIMIT THEOREM AND WHY IS IT IMPORTANT? ........................................................................ 6
Q2. WHAT IS SAMPLING? HOW MANY SAMPLING METHODS DO YOU KNOW? ................................................................... 7
Q3. WHAT IS THE DIFFERENCE BETWEEN TYPE I VS TYPE II ERROR? .................................................................................. 9
Q4. WHAT IS LINEAR REGRESSION? WHAT DO THE TERMS P-VALUE, COEFFICIENT, AND R-SQUARED VALUE MEAN? WHAT IS THE
SIGNIFICANCE OF EACH OF THESE COMPONENTS? ................................................................................................................. 9
Q5. WHAT ARE THE ASSUMPTIONS REQUIRED FOR LINEAR REGRESSION? ........................................................................ 10
Q6. WHAT IS A STATISTICAL INTERACTION? .............................................................................................................. 10
Q7. WHAT IS SELECTION BIAS? .............................................................................................................................. 11
Q8. WHAT IS AN EXAMPLE OF A DATA SET WITH A NON-GAUSSIAN DISTRIBUTION? .......................................................... 11
DATA SCIENCE .................................................................................................................................................... 12
Q1. WHAT IS DATA SCIENCE? LIST THE DIFFERENCES BETWEEN SUPERVISED AND UNSUPERVISED LEARNING. ......................... 12
Q2. WHAT IS SELECTION BIAS? ............................................................................................................................. 12
Q3. WHAT IS BIAS-VARIANCE TRADE-OFF? ............................................................................................................... 12
Q4. WHAT IS A CONFUSION MATRIX? ..................................................................................................................... 13
Q5. WHAT IS THE DIFFERENCE BETWEEN “LONG” AND “WIDE” FORMAT DATA? ............................................................... 14
Q6. WHAT DO YOU UNDERSTAND BY THE TERM NORMAL DISTRIBUTION? ...................................................................... 15
Q7. WHAT IS CORRELATION AND COVARIANCE IN STATISTICS? ...................................................................................... 15
Q8. WHAT IS THE DIFFERENCE BETWEEN POINT ESTIMATES AND CONFIDENCE INTERVAL? ................................................. 16
Q9. WHAT IS THE GOAL OF A/B TESTING? ............................................................................................................... 16
Q10. WHAT IS P-VALUE? ....................................................................................................................................... 16
Q11. IN ANY 15-MINUTE INTERVAL, THERE IS A 20% PROBABILITY THAT YOU WILL SEE AT LEAST ONE SHOOTING STAR. WHAT IS THE
PROBABILITY THAT YOU SEE AT LEAST ONE SHOOTING STAR IN THE PERIOD OF AN HOUR? ........................................................... 16
Q12. HOW CAN YOU GENERATE A RANDOM NUMBER BETWEEN 1 – 7 WITH ONLY A DIE? .................................................... 17
Q13. A CERTAIN COUPLE TELLS YOU THAT THEY HAVE TWO CHILDREN, AT LEAST ONE OF WHICH IS A GIRL. WHAT IS THE
PROBABILITY THAT THEY HAVE TWO GIRLS? ....................................................................................................................... 17
Q14. A JAR HAS 1000 COINS, OF WHICH 999 ARE FAIR AND 1 IS DOUBLE HEADED. PICK A COIN AT RANDOM AND TOSS IT 10
TIMES. GIVEN THAT YOU SEE 10 HEADS, WHAT IS THE PROBABILITY THAT THE NEXT TOSS OF THAT COIN IS ALSO A HEAD? ................. 17
Q15. WHAT DO YOU UNDERSTAND BY STATISTICAL POWER OF SENSITIVITY AND HOW DO YOU CALCULATE IT? ......................... 18
Q16. WHY IS RE-SAMPLING DONE? ......................................................................................................................... 18
Q17. WHAT ARE THE DIFFERENCES BETWEEN OVER-FITTING AND UNDER-FITTING? ............................................................ 19
Q18. HOW TO COMBAT OVERFITTING AND UNDERFITTING? ......................................................................................... 19
Q19. WHAT IS REGULARIZATION? WHY IS IT USEFUL? .................................................................................................. 20
Q20. WHAT IS THE LAW OF LARGE NUMBERS? .......................................................................................................... 20
Q21. WHAT ARE CONFOUNDING VARIABLES? ........................................................................................................... 20
Q22. WHAT ARE THE TYPES OF BIASES THAT CAN OCCUR DURING SAMPLING? ............................................................... 20
Q23. WHAT IS SURVIVORSHIP BIAS? ........................................................................................................................ 20
Q24. WHAT IS SELECTION BIAS? WHAT IS UNDER COVERAGE BIAS? ............................................................................... 21
Q25. EXPLAIN HOW A ROC CURVE WORKS? .............................................................................................................. 21
Q26. WHAT IS TF/IDF VECTORIZATION? .................................................................................................................. 22
Q27. WHY WE GENERALLY USE SOFT-MAX (OR SIGMOID) NON-LINEARITY FUNCTION AS LAST OPERATION IN-NETWORK? WHY
RELU IN AN INNER LAYER? ............................................................................................................................................ 22
DATA ANALYSIS .................................................................................................................................................. 23
Q1. PYTHON OR R – WHICH ONE WOULD YOU PREFER FOR TEXT ANALYTICS? ................................................................. 23
Q2. HOW DOES DATA CLEANING PLAY A VITAL ROLE IN THE ANALYSIS? ........................................................................... 23
Q3. DIFFERENTIATE BETWEEN UNIVARIATE, BIVARIATE AND MULTIVARIATE ANALYSIS. ....................................................... 23
Q4. EXPLAIN STAR SCHEMA. ................................................................................................................................. 23
Q5. WHAT IS CLUSTER SAMPLING? ........................................................................................................................ 23
Q6. WHAT IS SYSTEMATIC SAMPLING? ................................................................................................................... 24
Q7. WHAT ARE EIGENVECTORS AND EIGENVALUES? .................................................................................................. 24
Q8. CAN YOU CITE SOME EXAMPLES WHERE A FALSE POSITIVE IS IMPORTANT THAN A FALSE NEGATIVE? ................................ 24
Q9. CAN YOU CITE SOME EXAMPLES WHERE A FALSE NEGATIVE IMPORTANT THAN A FALSE POSITIVE? AND VICE VERSA? .......... 24
Q10. CAN YOU CITE SOME EXAMPLES WHERE BOTH FALSE POSITIVE AND FALSE NEGATIVES ARE EQUALLY IMPORTANT? ............. 25
Q11. CAN YOU EXPLAIN THE DIFFERENCE BETWEEN A VALIDATION SET AND A TEST SET? .................................................... 25
Q12. EXPLAIN CROSS-VALIDATION. .......................................................................................................................... 25
MACHINE LEARNING .......................................................................................................................................... 27
Q1. WHAT IS MACHINE LEARNING? ....................................................................................................................... 27
Q2. WHAT IS SUPERVISED LEARNING? .................................................................................................................... 27
Q3. WHAT IS UNSUPERVISED LEARNING? ................................................................................................................ 27
Q4. WHAT ARE THE VARIOUS ALGORITHMS? ............................................................................................................ 27
Q5. WHAT IS ‘NAIVE’ IN A NAIVE BAYES? ................................................................................................................ 28
Q6. WHAT IS PCA? WHEN DO YOU USE IT? ............................................................................................................. 29
Q7. EXPLAIN SVM ALGORITHM IN DETAIL. ............................................................................................................... 30
Q8. WHAT ARE THE SUPPORT VECTORS IN SVM? ...................................................................................................... 31
Q9. WHAT ARE THE DIFFERENT KERNELS IN SVM? .................................................................................................... 32
Q10. WHAT ARE THE MOST KNOWN ENSEMBLE ALGORITHMS? ...................................................................................... 32
Q11. EXPLAIN DECISION TREE ALGORITHM IN DETAIL. .................................................................................................. 32
Q12. WHAT ARE ENTROPY AND INFORMATION GAIN IN DECISION TREE ALGORITHM? ........................................................ 33
Gini Impurity and Information Gain - CART ....................................................................................................... 34
Entropy and Information Gain – ID3 .................................................................................................................. 37
Q13. WHAT IS PRUNING IN DECISION TREE? .............................................................................................................. 41
Q14. WHAT IS LOGISTIC REGRESSION? STATE AN EXAMPLE WHEN YOU HAVE USED LOGISTIC REGRESSION RECENTLY. ................ 41
Q15. WHAT IS LINEAR REGRESSION? ........................................................................................................................ 42
Q16. WHAT ARE THE DRAWBACKS OF THE LINEAR MODEL? ......................................................................................... 43
Q17. WHAT IS THE DIFFERENCE BETWEEN REGRESSION AND CLASSIFICATION ML TECHNIQUES? ........................................... 43
Q18. WHAT ARE RECOMMENDER SYSTEMS? ............................................................................................................. 43
Q19. WHAT IS COLLABORATIVE FILTERING? AND A CONTENT BASED? ............................................................................. 44
Q20. HOW CAN OUTLIER VALUES BE TREATED? ........................................................................................................... 44
Q21. WHAT ARE THE VARIOUS STEPS INVOLVED IN AN ANALYTICS PROJECT? ..................................................................... 45
Q22. DURING ANALYSIS, HOW DO YOU TREAT MISSING VALUES? .................................................................................... 45
Q23. HOW WILL YOU DEFINE THE NUMBER OF CLUSTERS IN A CLUSTERING ALGORITHM? ..................................................... 45
Q24. WHAT IS ENSEMBLE LEARNING? ...................................................................................................................... 48
Q25. DESCRIBE IN BRIEF ANY TYPE OF ENSEMBLE LEARNING. ......................................................................................... 49
Bagging ............................................................................................................................................................. 49
Boosting ............................................................................................................................................................. 49
Q26. WHAT IS A RANDOM FOREST? HOW DOES IT WORK? ........................................................................................... 50
Q27. HOW DO YOU WORK TOWARDS A RANDOM FOREST? ......................................................................................... 51
Q28. WHAT CROSS-VALIDATION TECHNIQUE WOULD YOU USE ON A TIME SERIES DATA SET? ................................................ 52
Q29. WHAT IS A BOX-COX TRANSFORMATION? ......................................................................................................... 53
Q30. HOW REGULARLY MUST AN ALGORITHM BE UPDATED? ....................................................................................... 53
Q31. IF YOU ARE HAVING 4GB RAM IN YOUR MACHINE AND YOU WANT TO TRAIN YOUR MODEL ON 10GB DATA SET. HOW
WOULD YOU GO ABOUT THIS PROBLEM? HAVE YOU EVER FACED THIS KIND OF PROBLEM IN YOUR MACHINE LEARNING/DATA SCIENCE
EXPERIENCE SO FAR? .................................................................................................................................................... 53
DEEP LEARNING ................................................................................................................................................. 55
Q1. WHAT DO YOU MEAN BY DEEP LEARNING? ........................................................................................................ 55
Q2. WHAT IS THE DIFFERENCE BETWEEN MACHINE LEARNING AND DEEP LEARNING? ........................................................ 55
Q3. WHAT, IN YOUR OPINION, IS THE REASON FOR THE POPULARITY OF DEEP LEARNING IN RECENT TIMES? .......................... 56
Q4. WHAT IS REINFORCEMENT LEARNING? .............................................................................................................. 56
Q5. WHAT ARE ARTIFICIAL NEURAL NETWORKS? ...................................................................................................... 57
Q6. DESCRIBE THE STRUCTURE OF ARTIFICIAL NEURAL NETWORKS? ............................................................................. 57
Q7. HOW ARE WEIGHTS INITIALIZED IN A NETWORK? ............................................................................................... 57
Q8. WHAT IS THE COST FUNCTION? ....................................................................................................................... 58
Q9. WHAT ARE HYPERPARAMETERS? ..................................................................................................................... 58
Q10. WHAT WILL HAPPEN IF THE LEARNING RATE IS SET INACCURATELY (TOO LOW OR TOO HIGH)? ................................... 58
Q11. WHAT IS THE DIFFERENCE BETWEEN EPOCH, BATCH, AND ITERATION IN DEEP LEARNING? ......................................... 58
Q12. WHAT ARE THE DIFFERENT LAYERS ON CNN? .................................................................................................... 58
Convolution Operation ...................................................................................................................................... 60
Pooling Operation ............................................................................................................................................. 62
Classification ..................................................................................................................................................... 63
Training ............................................................................................................................................................. 64
Testing ............................................................................................................................................................... 65
Q13. WHAT IS POOLING ON CNN, AND HOW DOES IT WORK? .................................................................................... 65
Q14. WHAT ARE RECURRENT NEURAL NETWORKS (RNNS)? ........................................................................................ 65
Parameter Sharing ............................................................................................................................................ 67
Deep RNNs ......................................................................................................................................................... 68
Bidirectional RNNs ............................................................................................................................................. 68
Recursive Neural Network ................................................................................................................................. 69
Encoder Decoder Sequence to Sequence RNNs ................................................................................................. 70
LSTMs ................................................................................................................................................................ 70
Q15. HOW DOES AN LSTM NETWORK WORK? ......................................................................................................... 70
Recurrent Neural Networks ............................................................................................................................... 71
The Problem of Long-Term Dependencies ......................................................................................................... 72
LSTM Networks .................................................................................................................................................. 73
The Core Idea Behind LSTMs ............................................................................................................................. 74
Q16. WHAT IS A MULTI-LAYER PERCEPTRON (MLP)? ................................................................................................. 75
Q17. EXPLAIN GRADIENT DESCENT. ......................................................................................................................... 76
Q18. WHAT IS EXPLODING GRADIENTS? .................................................................................................................... 77
Solutions ............................................................................................................................................................ 78
Q19. WHAT IS VANISHING GRADIENTS? .................................................................................................................... 78
Solutions ............................................................................................................................................................ 79
Q20. WHAT IS BACK PROPAGATION AND EXPLAIN IT WORKS. ....................................................................................... 79
Q21. WHAT ARE THE VARIANTS OF BACK PROPAGATION? ............................................................................................ 79
Q22. WHAT ARE THE DIFFERENT DEEP LEARNING FRAMEWORKS? .................................................................................. 81
Q23. WHAT IS THE ROLE OF THE ACTIVATION FUNCTION? ............................................................................................ 81
Q24. NAME A FEW MACHINE LEARNING LIBRARIES FOR VARIOUS PURPOSES. .................................................................... 81
Q25. WHAT IS AN AUTO-ENCODER? ........................................................................................................................ 81
Q26. WHAT IS A BOLTZMANN MACHINE? ................................................................................................................. 82
Q27. WHAT IS DROPOUT AND BATCH NORMALIZATION? ............................................................................................. 83
Q28. WHY IS TENSORFLOW THE MOST PREFERRED LIBRARY IN DEEP LEARNING? ............................................................. 83
Q29. WHAT DO YOU MEAN BY TENSOR IN TENSORFLOW? .......................................................................................... 83
Q30. WHAT IS THE COMPUTATIONAL GRAPH? ........................................................................................................... 83
Q31. HOW IS LOGISTIC REGRESSION DONE? ............................................................................................................... 83
MISCELLANEOUS ................................................................................................................................................ 84
Q1. EXPLAIN THE STEPS IN MAKING A DECISION TREE. ................................................................................................. 84
Q2. HOW DO YOU BUILD A RANDOM FOREST MODEL? ................................................................................................ 84
Q3. DIFFERENTIATE BETWEEN UNIVARIATE, BIVARIATE, AND MULTIVARIATE ANALYSIS. ...................................................... 85
Univariate .......................................................................................................................................................... 85
Bivariate ............................................................................................................................................................ 85
Multivariate ....................................................................................................................................................... 85
Q4. WHAT ARE THE FEATURE SELECTION METHODS USED TO SELECT THE RIGHT VARIABLES? .............................................. 86
Filter Methods ................................................................................................................................................... 86
Wrapper Methods ............................................................................................................................................. 86
Q5. IN YOUR CHOICE OF LANGUAGE, WRITE A PROGRAM THAT PRINTS THE NUMBERS RANGING FROM ONE TO 50. BUT FOR
MULTIPLES OF THREE, PRINT "FIZZ" INSTEAD OF THE NUMBER AND FOR THE MULTIPLES OF FIVE, PRINT "BUZZ." FOR NUMBERS WHICH
ARE MULTIPLES OF BOTH THREE AND FIVE, PRINT "FIZZBUZZ." .............................................................................................. 86
Q6. YOU ARE GIVEN A DATA SET CONSISTING OF VARIABLES WITH MORE THAN 30 PERCENT MISSING VALUES. HOW WILL YOU
DEAL WITH THEM? ....................................................................................................................................................... 87
Q7. FOR THE GIVEN POINTS, HOW WILL YOU CALCULATE THE EUCLIDEAN DISTANCE IN PYTHON? ........................................ 87
Q8. WHAT ARE DIMENSIONALITY REDUCTION AND ITS BENEFITS? ................................................................................. 87
Q9. HOW WILL YOU CALCULATE EIGENVALUES AND EIGENVECTORS OF THE FOLLOWING 3X3 MATRIX? ................................. 88
Q10. HOW SHOULD YOU MAINTAIN A DEPLOYED MODEL? ............................................................................................ 88
Q11. HOW CAN A TIME-SERIES DATA BE DECLARED AS STATIONERY? ............................................................................... 88
Q12. 'PEOPLE WHO BOUGHT THIS ALSO BOUGHT...' RECOMMENDATIONS SEEN ON AMAZON ARE A RESULT OF WHICH ALGORITHM?
89
Q13. WHAT IS A GENERATIVE ADVERSARIAL NETWORK? .............................................................................................. 89
Q14. YOU ARE GIVEN A DATASET ON CANCER DETECTION. YOU HAVE BUILT A CLASSIFICATION MODEL AND ACHIEVED AN ACCURACY
OF 96 PERCENT. WHY SHOULDN'T YOU BE HAPPY WITH YOUR MODEL PERFORMANCE? WHAT CAN YOU DO ABOUT IT? ................... 90
Q15. BELOW ARE THE EIGHT ACTUAL VALUES OF THE TARGET VARIABLE IN THE TRAIN FILE. WHAT IS THE ENTROPY OF THE TARGET
VARIABLE? [0, 0, 0, 1, 1, 1, 1, 1] .................................................................................................................................. 90
Q16. WE WANT TO PREDICT THE PROBABILITY OF DEATH FROM HEART DISEASE BASED ON THREE RISK FACTORS: AGE, GENDER, AND
BLOOD CHOLESTEROL LEVEL. WHAT IS THE MOST APPROPRIATE ALGORITHM FOR THIS CASE? CHOOSE THE CORRECT OPTION: ........... 90
Q17. AFTER STUDYING THE BEHAVIOR OF A POPULATION, YOU HAVE IDENTIFIED FOUR SPECIFIC INDIVIDUAL TYPES THAT ARE
VALUABLE TO YOUR STUDY. YOU WOULD LIKE TO FIND ALL USERS WHO ARE MOST SIMILAR TO EACH INDIVIDUAL TYPE. WHICH
ALGORITHM IS MOST APPROPRIATE FOR THIS STUDY? .......................................................................................................... 90
Q18. YOU HAVE RUN THE ASSOCIATION RULES ALGORITHM ON YOUR DATASET, AND THE TWO RULES {BANANA, APPLE} => {GRAPE}
AND {APPLE, ORANGE} => {GRAPE} HAVE BEEN FOUND TO BE RELEVANT. WHAT ELSE MUST BE TRUE? CHOOSE THE RIGHT ANSWER: .. 90
Q19. YOUR ORGANIZATION HAS A WEBSITE WHERE VISITORS RANDOMLY RECEIVE ONE OF TWO COUPONS. IT IS ALSO POSSIBLE THAT
VISITORS TO THE WEBSITE WILL NOT RECEIVE A COUPON. YOU HAVE BEEN ASKED TO DETERMINE IF OFFERING A COUPON TO WEBSITE
VISITORS HAS ANY IMPACT ON THEIR PURCHASE DECISIONS. WHICH ANALYSIS METHOD SHOULD YOU USE? .................................... 91
Q20. WHAT ARE THE FEATURE VECTORS? .................................................................................................................. 91
Q21. WHAT IS ROOT CAUSE ANALYSIS? ..................................................................................................................... 91
Q22. DO GRADIENT DESCENT METHODS ALWAYS CONVERGE TO SIMILAR POINTS? ............................................................. 91
Q23. WHAT ARE THE MOST POPULAR CLOUD SERVICES USED IN DATA SCIENCE? .............................................................. 91
Q24. WHAT IS A CANARY DEPLOYMENT? .................................................................................................................. 92
Q25. WHAT IS A BLUE GREEN DEPLOYMENT? ............................................................................................................ 93
Data Science interview questions
Statistics
Q1. What is the Central Limit Theorem and why is it important?
https://spin.atomicobject.com/2015/02/12/central-limit-theorem-intro/
Suppose that we are interested in estimating the average height among all people. Collecting data for
every person in the world is impractical, bordering on impossible. While we can’t obtain a height
measurement from everyone in the population, we can still sample some people. The question now
becomes, what can we say about the average height of the entire population given a single sample.
The Central Limit Theorem addresses this question exactly. Formally, it states that if we sample from a
population using a sufficiently large sample size, the mean of the samples (also known as the sample
population) will be normally distributed (assuming true random sampling), the mean tending to the mean
of the population and variance equal to the variance of the population divided by the size of the sampling.
What’s especially important is that this will be true regardless of the distribution of the original
population.
EX:
As we can see, the distribution is pretty ugly. It certainly isn’t normal, uniform, or any other commonly
known distribution. In order to sample from the above distribution, we need to define a sample size,
referred to as N. This is the number of observations that we will sample at a time. Suppose that we choose
N to be 3. This means that we will sample in groups of 3. So for the above population, we might sample
groups such as [5, 20, 41], [60, 17, 82], [8, 13, 61], and so on.
Suppose that we gather 1,000 samples of 3 from the above population. For each sample, we can compute
its average. If we do that, we will have 1,000 averages. This set of 1,000 averages is called a sampling
distribution, and according to Central Limit Theorem, the sampling distribution will approach a normal
distribution as the sample size N used to produce it increases. Here is what our sample distribution looks
like for N = 3.
As we can see, it certainly looks uni-modal, though not necessarily normal. If we repeat the same process
with a larger sample size, we should see the sampling distribution start to become more normal. Let’s
repeat the same process again with N = 10. Here is the sampling distribution for that sample size.
Q2. What is sampling? How many sampling methods do you know?
https://searchbusinessanalytics.techtarget.com/definition/data-sampling
https://nikolanews.com/difference-between-stratified-sampling-cluster-sampling-and-quota-sampling/
Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative
subset of data points to identify patterns and trends in the larger data set being examined. It enables data
scientists, predictive modelers and other data analysts to work with a small, manageable amount of data
about a statistical population to build and run analytical models more quickly, while still producing
accurate findings.
Sampling can be particularly useful with data sets that are too large to efficiently analyze in full – for
example, in big data analytics applications or surveys. Identifying and analyzing a representative sample
is more efficient and cost-effective than surveying the entirety of the data or population.
An important consideration, though, is the size of the required data sample and the possibility of
introducing a sampling error. In some cases, a small sample can reveal the most important information
about a data set. In others, using a larger sample can increase the likelihood of accurately representing
the data as a whole, even though the increased size of the sample may impede ease of manipulation and
interpretation.
There are many different methods for drawing samples from data; the ideal one depends on the data set
and situation. Sampling can be based on probability, an approach that uses random numbers that
correspond to points in the data set to ensure that there is no correlation between points chosen for the
sample. Further variations in probability sampling include:
• Simple random sampling: Software is used to randomly select subjects from the whole population.
• Stratified sampling: Subsets of the data sets or population are created based on a common factor,
and samples are randomly collected from each subgroup. A sample is drawn from each strata
(using a random sampling method like simple random sampling or systematic sampling).
o EX: In the image below, let's say you need a sample size of 6. Two members from each
group (yellow, red, and blue) are selected randomly. Make sure to sample proportionally:
In this simple example, 1/3 of each group (2/6 yellow, 2/6 red and 2/6 blue) has been
sampled. If you have one group that's a different size, make sure to adjust your
proportions. For example, if you had 9 yellow, 3 red and 3 blue, a 5-item sample would
consist of 3/9 yellow (i.e. one third), 1/3 red and 1/3 blue.
• Cluster sampling: The larger data set is divided into subsets (clusters) based on a defined factor,
then a random sampling of clusters is analyzed. The sampling unit is the whole cluster; Instead of
sampling individuals from within each group, a researcher will study whole clusters.
o EX: In the image below, the strata are natural groupings by head color (yellow, red, blue).
A sample size of 6 is needed, so two of the complete strata are selected randomly (in this
example, groups 2 and 4 are chosen).
• Multistage sampling: A more complicated form of cluster sampling, this method also involves
dividing the larger population into a number of clusters. Second-stage clusters are then broken
out based on a secondary factor, and those clusters are then sampled and analyzed. This staging
could continue as multiple subsets are identified, clustered and analyzed.
• Systematic sampling: A sample is created by setting an interval at which to extract data from the
larger population – for example, selecting every 10th row in a spreadsheet of 200 items to create
a sample size of 20 rows to analyze.
Sampling can also be based on non-probability, an approach in which a data sample is determined and
extracted based on the judgment of the analyst. As inclusion is determined by the analyst, it can be more
difficult to extrapolate whether the sample accurately represents the larger population than when
probability sampling is used.
Non-probability data sampling methods include:
• Convenience sampling: Data is collected from an easily accessible and available group.
• Consecutive sampling: Data is collected from every subject that meets the criteria until the
predetermined sample size is met.
• Purposive or judgmental sampling: The researcher selects the data to sample based on predefined
criteria.
• Quota sampling: The researcher ensures equal representation within the sample for all subgroups
in the data set or population (random sampling is not used).
Once generated, a sample can be used for predictive analytics. For example, a retail business might use
data sampling to uncover patterns about customer behavior and predictive modeling to create more
effective sales strategies.
Q3. What is the difference between type I vs type II error?
https://www.datasciencecentral.com/profiles/blogs/understanding-type-i-and-type-ii-errors
Is H
a
true? No, H
0
is True (H
a
is Negative: TN); Yes, H
0
is False (H
a
is Positive: TP).
A type I error occurs when the null hypothesis is true but is rejected. A type II error occurs when the null
hypothesis is false but erroneously fails to be rejected.
No reject H
0
Reject H
0
H
0
is True TN FP (I error)
H
0
is False FN (II error) TP
Q4. What is linear regression? What do the terms p-value, coefficient, and r-
squared value mean? What is the significance of each of these components?
https://www.springboard.com/blog/linear-regression-in-python-a-tutorial/
https://blog.minitab.com/blog/adventures-in-statistics-2/how-to-interpret-regression-analysis-results-p-
values-and-coefficients
Imagine you want to predict the price of a house. That will depend on some factors, called independent
variables, such as location, size, year of construction… if we assume there is a linear relationship between
these variables and the price (our dependent variable), then our price is predicted by the following
function:
= +
The p-value in the table is the minimum (the significance level) at which the coefficient is relevant. The
lower the p-value, the more important is the variable in predicting the price. Usually we set a 5% level, so
that we have a 95% confidentiality that our variable is relevant.
The p-value is used as an alternative to rejection points to provide the smallest level of significance at
which the null hypothesis would be rejected. A smaller p-value means that there is stronger evidence in
favor of the alternative hypothesis.
The coefficient value signifies how much the mean of the dependent variable changes given a one-unit
shift in the independent variable while holding other variables in the model constant. This property of
holding the other variables constant is crucial because it allows you to assess the effect of each variable
in isolation from the others.
R squared (R
2
) is a statistical measure that represents the proportion of the variance for a dependent
variable that's explained by an independent variable or variables in a regression model.
Q5. What are the assumptions required for linear regression?
There are four major assumptions:
• There is a linear relationship between the dependent variables and the regressors, meaning the
model you are creating actually fits the data,
• The errors or residuals (
−
) of the data are normally distributed and independent from
each other,
• There is minimal multicollinearity between explanatory variables, and
• Homoscedasticity. This means the variance around the regression line is the same for all values
of the predictor variable.
Q6. What is a statistical interaction?
http://icbseverywhere.com/blog/mini-lessons-tutorials-and-support-pages/statistical-interactions/
Basically, an interaction is when the effect of one factor (input variable) on the dependent variable (output
variable) differs among levels of another factor. When two or more independent variables are involved in
a research design, there is more to consider than simply the "main effect" of each of the independent
variables (also termed "factors"). That is, the effect of one independent variable on the dependent
variable of interest may not be the same at all levels of the other independent variable. Another way to
put this is that the effect of one independent variable may depend on the level of the other independent
variable. In order to find an interaction, you must have a factorial design, in which the two (or more)
independent variables are "crossed" with one another so that there are observations at every
combination of levels of the two independent variables. EX: stress level and practice to memorize words:
together they may have a lower performance.
Q7. What is selection bias?
https://www.elderresearch.com/blog/selection-bias-in-analytics
Selection (or ‘sampling’) bias occurs when the sample data that is gathered and prepared for modeling
has characteristics that are not representative of the true, future population of cases the model will see.
That is, active selection bias occurs when a subset of the data is systematically (i.e., non-randomly)
excluded from analysis.
Q8. What is an example of a data set with a non-Gaussian distribution?
https://www.quora.com/Most-machine-learning-datasets-are-in-Gaussian-distribution-Where-can-we-find-
the-dataset-which-follows-Bernoulli-Poisson-gamma-beta-etc-distribution
The Gaussian distribution is part of the Exponential family of distributions, but there are a lot more of
them, with the same sort of ease of use, in many cases, and if the person doing the machine learning has
a solid grounding in statistics, they can be utilized where appropriate.
Binomial: multiple toss of a coin Bin(n,p): the binomial distribution consists of the probabilities of each of
the possible numbers of successes on n trials for independent events that each have a probability of p of
occurring.
Bernoulli: Bin(1,p) = Be(p)
Poisson: Pois()
Data Science
Q1. What is Data Science? List the differences between supervised and
unsupervised learning.
Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to
discover hidden patterns from the raw data. How is this different from what statisticians have been doing
for years? The answer lies in the difference between explaining and predicting: statisticians work a
posteriori, explaining the results and designing a plan; data scientists use historical data to make
predictions.
The differences between supervised and unsupervised learning are:
Supervised Unsupervised
Input data is labelled Input data is unlabeled
Split in training/validation/test No split
Used for prediction Used for analysis
Classification and Regression
Clustering, dimension reduction,
and density estimation
Q2. What is Selection Bias?
Selection bias is a kind of error that occurs when the researcher decides what has to be studied. It is
associated with research where the selection of participants is not random. Therefore, some conclusions
of the study may not be accurate.
The types of selection bias include:
• Sampling bias: It is a systematic error due to a non-random sample of a population causing some
members of the population to be less likely to be included than others resulting in a biased
sample.
• Time interval: A trial may be terminated early at an extreme value (often for ethical reasons), but
the extreme value is likely to be reached by the variable with the largest variance, even if all
variables have a similar mean.
• Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data
on arbitrary grounds, instead of according to previously stated or generally agreed criteria.
• Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants)
discounting trial subjects/tests that did not run to completion.
Q3. What is bias-variance trade-off?
Bias: Bias is an error introduced in the model due to the oversimplification of the algorithm used (does
not fit the data properly). It can lead to under-fitting.
Low bias machine learning algorithms — Decision Trees, k-NN and SVM
High bias machine learning algorithms — Linear Regression, Logistic Regression
Variance: Variance is error introduced in the model due to a too complex algorithm, it performs very well
in the training set but poorly in the test set. It can lead to high sensitivity and overfitting.
Possible high variance – polynomial regression
Normally, as you increase the complexity of your model, you will see a reduction in error due to lower
bias in the model. However, this only happens until a particular point. As you continue to make your model
more complex, you end up over-fitting your model and hence your model will start suffering from high
variance.
Bias-Variance trade-off: The goal of any supervised machine learning algorithm is to have low bias and
low variance to achieve good prediction performance.
1. The k-nearest neighbor algorithm has low bias and high variance, but the trade-off can be changed
by increasing the value of k which increases the number of neighbors that contribute to the
prediction and in turn increases the bias of the model.
2. The support vector machine algorithm has low bias and high variance, but the trade-off can be
changed by increasing the C parameter that influences the number of violations of the margin
allowed in the training data which increases the bias but decreases the variance.
3. The decision tree has low bias and high variance, you can decrease the depth of the tree or use
fewer attributes.
4. The linear regression has low variance and high bias, you can increase the number of features or
use another regression that better fits the data.
There is no escaping the relationship between bias and variance in machine learning. Increasing the bias
will decrease the variance. Increasing the variance will decrease bias.
Q4. What is a confusion matrix?
The confusion matrix is a 2X2 table that contains 4 outputs provided by the binary classifier.
Predict + Predict -
Actual + TP FN (II error)
Actual - FP (I error) TN
A data set used for performance evaluation is called a test data set. It should contain the correct labels
and predicted labels. The predicted labels will exactly the same if the performance of a binary classifier is
perfect. The predicted labels usually match with part of the observed labels in real-world scenarios.
A binary classifier predicts all data instances of a test data set as either positive or negative. This produces
four outcomes: TP, FP, TN, FN. Basic measures derived from the confusion matrix:
1. =
2. =
3.
(
)
=
=
4.
(
)
=
=
5.
(
)
=
6. −
(
)
=
(
)
Q5. What is the difference between “long” and “wide” format data?
In the wide-format, a subject’s repeated responses will be in a single row, and each response is in a
separate column. In the long-format, each row is a one-time point per subject. You can recognize data in
wide format by the fact that columns generally represent groups (variables).
Q6. What do you understand by the term Normal Distribution?
Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled
up. However, there are chances that data is distributed around a central value without any bias to the left
or right and reaches normal distribution in the form of a bell-shaped curve.
The random variables are distributed in the form of a symmetrical, bell-shaped curve. Properties of
Normal Distribution are as follows:
1. Unimodal (Only one mode)
2. Symmetrical (left and right halves are mirror images)
3. Bell-shaped (maximum height (mode) at the mean)
4. Mean, Mode, and Median are all located in the center
5. Asymptotic
Q7. What is correlation and covariance in statistics?
Correlation is considered or described as the best technique for measuring and also for estimating the
quantitative relationship between two variables. Correlation measures how strongly two variables are
related. Given two random variables, it is the covariance between both divided by the product of the two
standard deviations of the single variables, hence always between -1 and 1.
=
(
,
)
(
)
(
)
∈ [−1,1]
Covariance is a measure that indicates the extent to which two random variables change in cycle. It
explains the systematic relation between a pair of random variables, wherein changes in one variable
reciprocal by a corresponding change in another variable.
(
,
)
=
[
(
−
[
]
)(
−
[
]
)
]
=
[
]
−
[
]
[
]
Q8. What is the difference between Point Estimates and Confidence Interval?
Point Estimation gives us a particular value as an estimate of a population parameter. Method of Moments
and Maximum Likelihood estimator methods are used to derive Point Estimators for population
parameters.
A confidence interval gives us a range of values which is likely to contain the population parameter. The
confidence interval is generally preferred, as it tells us how likely this interval is to contain the population
parameter. This likeliness or probability is called Confidence Level or Confidence coefficient and
represented by 1 −, where is the level of significance.
Q9. What is the goal of A/B Testing?
It is a hypothesis testing for a randomized experiment with two variables A and B.
The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome
of interest. A/B testing is a fantastic method for figuring out the best online promotional and marketing
strategies for your business. It can be used to test everything from website copy to sales emails to search
ads. An example of this could be identifying the click-through rate for a banner ad.
Q10. What is p-value?
When you perform a hypothesis test in statistics, a p-value can help you determine the strength of your
results. p-value is the minimum significance level at which you can reject the null hypothesis. The lower
the p-value, the more likely you reject the null hypothesis.
Q11. In any 15-minute interval, there is a 20% probability that you will see at least
one shooting star. What is the probability that you see at least one shooting star in
the period of an hour?
• ℎ 15 =
1 – ( ℎ ) = 1 – 0.2 = 0.8
• ℎ ℎ ℎ =
(
0.8
)
=
0.4096
• ℎ ℎ ℎ =
1 – ( ) = 1 – 0.4096 = 0.5904
Q12. How can you generate a random number between 1 – 7 with only a die?
Any die has six sides from 1-6. There is no way to get seven equal outcomes from a single rolling of a die.
If we roll the die twice and consider the event of two rolls, we now have 36 different outcomes. To get
our 7 equal outcomes we have to reduce this 36 to a number divisible by 7. We can thus consider only 35
outcomes and exclude the other one. A simple scenario can be to exclude the combination (6,6), i.e., to
roll the die again if 6 appears twice. All the remaining combinations from (1,1) till (6,5) can be divided into
7 parts of 5 each. This way all the seven sets of outcomes are equally likely.
Q13. A certain couple tells you that they have two children, at least one of which
is a girl. What is the probability that they have two girls?
(
)
=
1
2
Q14. A jar has 1000 coins, of which 999 are fair and 1 is double headed. Pick a coin
at random and toss it 10 times. Given that you see 10 heads, what is the probability
that the next toss of that coin is also a head?
There are two ways of choosing the coin. One is to pick a fair coin and the other is to pick the one with
two heads.
=
999
1000
= 0.999
=
1
1000
= 0.001
10 ℎ
= ∗ 10 ℎ +
=
(
)
+
(
)
(
)
= 0.999 ∗
1
2
= 0.999 ∗
1
1024
= 0.000976
(
)
= 0.001 ∗ 1 = 0.001
(
)
(
)
+
(
)
=
0.000976
0.000976 + 0.001
= 0.4939
(
)
(
)
+ ()
=
0.001
0.001976
= 0.5061
ℎ ℎ =
(
)
(
)
+ ()
∗ 0.5 +
(
)
(
)
+ ()
∗ 1 =
= 0.4939 ∗ 0.5 + 0.5061 = 0.7531
Q15. What do you understand by statistical power of sensitivity and how do you
calculate it?
Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest etc.).
=
+
Q16. Why is Re-sampling done?
https://machinelearningmastery.com/statistical-sampling-and-resampling/
• Sampling is an active process of gathering observations with the intent of estimating a population
variable.
• Resampling is a methodology of economically using a data sample to improve the accuracy and
quantify the uncertainty of a population parameter. Resampling methods, in fact, make use of a
nested resampling method.
Once we have a data sample, it can be used to estimate the population parameter. The problem is that
we only have a single estimate of the population parameter, with little idea of the variability or uncertainty
in the estimate. One way to address this is by estimating the population parameter multiple times from
our data sample. This is called resampling. Statistical resampling methods are procedures that describe
how to economically use available data to estimate a population parameter. The result can be both a
more accurate estimate of the parameter (such as taking the mean of the estimates) and a quantification
of the uncertainty of the estimate (such as adding a confidence interval).
Resampling methods are very easy to use, requiring little mathematical knowledge. A downside of the
methods is that they can be computationally very expensive, requiring tens, hundreds, or even thousands
of resamples in order to develop a robust estimate of the population parameter.
The key idea is to resample form the original data — either directly or via a fitted model — to create
replicate datasets, from which the variability of the quantiles of interest can be assessed without long-
winded and error-prone analytical calculation. Because this approach involves repeating the original data
analysis procedure with many replicate sets of data, these are sometimes called computer-intensive
methods. Each new subsample from the original data sample is used to estimate the population
parameter. The sample of estimated population parameters can then be considered with statistical tools
in order to quantify the expected value and variance, providing measures of the uncertainty of the
estimate. Statistical sampling methods can be used in the selection of a subsample from the original
sample.
A key difference is that process must be repeated multiple times. The problem with this is that there will
be some relationship between the samples as observations that will be shared across multiple
subsamples. This means that the subsamples and the estimated population parameters are not strictly
identical and independently distributed. This has implications for statistical tests performed on the sample
of estimated population parameters downstream, i.e. paired statistical tests may be required.
Two commonly used resampling methods that you may encounter are k-fold cross-validation and the
bootstrap.
• Bootstrap. Samples are drawn from the dataset with replacement (allowing the same sample to
appear more than once in the sample), where those instances not drawn into the data sample
may be used for the test set.
• k-fold Cross-Validation. A dataset is partitioned into k groups, where each group is given the
opportunity of being used as a held out test set leaving the remaining groups as the training set.
The k-fold cross-validation method specifically lends itself to use in the evaluation of predictive
models that are repeatedly trained on one subset of the data and evaluated on a second held-out
subset of the data.
Resampling is done in any of these cases:
• Estimating the accuracy of sample statistics by using subsets of accessible data or drawing
randomly with replacement from a set of data points
• Substituting labels on data points when performing significance tests
• Validating models by using random subsets (bootstrapping, cross-validation)
Q17. What are the differences between over-fitting and under-fitting?
In statistics and machine learning, one of the most common tasks is to fit a model to a set of training data,
so as to be able to make reliable predictions on general untrained data.
In overfitting, a statistical model describes random error or noise instead of the underlying relationship.
Overfitting occurs when a model is excessively complex, such as having too many parameters relative to
the number of observations. A model that has been overfitted, has poor predictive performance, as it
overreacts to minor fluctuations in the training data.
Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying
trend of the data. Underfitting would occur, for example, when fitting a linear model to non-linear data.
Such a model too would have poor predictive performance.
Q18. How to combat Overfitting and Underfitting?
To combat overfitting:
1. Add noise
2. Feature selection
3. Increase training set
4. L2 (ridge) or L1 (lasso) regularization; L1 drops weights, L2 no
5. Use cross-validation techniques, such as k folds cross-validation
6. Boosting and bagging
7. Dropout technique
8. Perform early stopping
9. Remove inner layers
To combat underfitting:
1. Add features
2. Increase time of training
Q19. What is regularization? Why is it useful?
Regularization is the process of adding tuning parameter (penalty term) to a model to induce smoothness
in order to prevent overfitting. This is most often done by adding a constant multiple to an existing weight
vector. This constant is often the L1 (Lasso - ||) or L2 (Ridge -
). The model predictions should then
minimize the loss function calculated on the regularized training set.
Q20. What Is the Law of Large Numbers?
It is a theorem that describes the result of performing the same experiment a large number of times. This
theorem forms the basis of frequency-style thinking. It says that the sample means, the sample variance
and the sample standard deviation converge to what they are trying to estimate. According to the law,
the average of the results obtained from a large number of trials should be close to the expected value
and will tend to become closer to the expected value as more trials are performed.
Q21. What Are Confounding Variables?
In statistics, a confounder is a variable that influences both the dependent variable and independent
variable.
If you are researching whether a lack of exercise leads to weight gain:
lack of exercise = independent variable
weight gain = dependent variable
A confounding variable here would be any other variable that affects both of these variables, such as the
age of the subject.
Q22. What Are the Types of Biases That Can Occur During Sampling?
a. Selection bias
b. Under coverage bias
c. Survivorship bias
Q23. What is Survivorship Bias?
It is the logical error of focusing aspects that support surviving some process and casually overlooking
those that did not work because of their lack of prominence. This can lead to wrong conclusions in
numerous different means. For example, during a recession you look just at the survived businesses, noting
that they are performing poorly. However, they perform better than the rest, which is failed, thus being
removed from the time series.
Q24. What is Selection Bias? What is under coverage bias?
https://stattrek.com/survey-research/survey-bias.aspx
Selection bias occurs when the sample obtained is not representative of the population intended to be
analyzed. For instance, you select only Asians to perform a study on the world population height.
Under coverage bias occurs when some members of the population are inadequately represented in the
sample. A classic example of under coverage is the Literary Digest voter survey, which predicted that Alfred
Landon would beat Franklin Roosevelt in the 1936 presidential election. The survey sample suffered from
under coverage of low-income voters, who tended to be Democrats.
How did this happen? The survey relied on a convenience sample, drawn from telephone directories and
car registration lists. In 1936, people who owned cars and telephones tended to be more affluent. Under
coverage is often a problem with convenience samples.
Q25. Explain how a ROC curve works?
The ROC curve is a graphical representation of the contrast between true positive rates and false positive
rates at various thresholds. It is often used as a proxy for the trade-off between the sensitivity (true
positive rate) and false positive rate.
• =
=
• =
=
• =
• =
Q26. What is TF/IDF vectorization?
TF-IDF is short for term frequency-inverse document frequency, is a numerical statistic that is intended to
reflect how important a word is to a document in a collection or corpus. It is often used as a weighting
factor in information retrieval and text mining.
• =
# ‘’
#
• =
# ‘’
The TF-IDF value increases proportionally to the number of times a word appears in the document but is
offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words
appear more frequently in general.
Q27. Why we generally use Soft-max (or sigmoid) non-linearity function as last
operation in-network? Why RELU in an inner layer?
It is because it takes in a vector of real numbers and returns a probability distribution. Its definition is as
follows. Let x be a vector of real numbers (positive, negative, whatever, there are no constraints).
Then the i-eth component of soft-max(x) is:
It should be clear that the output is a probability distribution: each element is non-negative and the sum
over all components is 1.
RELU because it avoids the vanishing gradient descent issue.
Data Analysis
Q1. Python or R – Which one would you prefer for text analytics?
We will prefer Python because of the following reasons:
• Python would be the best option because it has Pandas library that provides easy to use data
structures and high-performance data analysis tools.
• R is more suitable for machine learning than just text analysis.
• Python performs faster for all types of text analytics.
Q2. How does data cleaning play a vital role in the analysis?
Data cleaning can help in analysis because:
• Cleaning data from multiple sources helps transform it into a format that data analysts or data
scientists can work with.
• Data Cleaning helps increase the accuracy of the model in machine learning.
• It is a cumbersome process because as the number of data sources increases, the time taken to
clean the data increases exponentially due to the number of sources and the volume of data
generated by these sources.
• It might take up to 80% of the time for just cleaning data making it a critical part of the analysis
task.
Q3. Differentiate between univariate, bivariate and multivariate analysis.
Univariate analyses are descriptive statistical analysis techniques which can be differentiated based on
one variable involved at a given point of time. For example, the pie charts of sales based on territory
involve only one variable and can the analysis can be referred to as univariate analysis.
The bivariate analysis attempts to understand the difference between two variables at a time as in a
scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of
bivariate analysis.
Multivariate analysis deals with the study of more than two variables to understand the effect of variables
on the responses.
Q4. Explain Star Schema.
It is a traditional database schema with a central table. Satellite tables map IDs to physical names or
descriptions and can be connected to the central fact table using the ID fields; these tables are known as
lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes
star schemas involve several layers of summarization to recover information faster.
Q5. What is Cluster Sampling?
Cluster sampling is a technique used when it becomes difficult to study the target population spread
across a wide area and simple random sampling cannot be applied. Cluster Sample is a probability sample
where each sampling unit is a collection or cluster of elements.
For example, a researcher wants to survey the academic performance of high school students in Japan. He
can divide the entire population of Japan into different clusters (cities). Then the researcher selects a
number of clusters depending on his research through simple or systematic random sampling.
Q6. What is Systematic Sampling?
Systematic sampling is a statistical technique where elements are selected from an ordered sampling
frame. In systematic sampling, the list is progressed in a circular manner so once you reach the end of the
list, it is progressed from the top again. The best example of systematic sampling is equal probability
method.
Q7. What are Eigenvectors and Eigenvalues?
Eigenvectors are used for understanding linear transformations. In data analysis, we usually calculate the
eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a
particular linear transformation acts by flipping, compressing or stretching.
Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the
factor by which the compression occurs.
Q8. Can you cite some examples where a false positive is important than a false
negative?
Let us first understand what false positives and false negatives are
• False Positives are the cases where you wrongly classified a non-event as an event a.k.a Type I
error.
• False Negatives are the cases where you wrongly classify events as non-events, a.k.a Type II error.
Example 1: In the medical field, assume you have to give chemotherapy to patients. Assume a patient
comes to that hospital and he is tested positive for cancer, based on the lab prediction but he actually
doesn’t have cancer. This is a case of false positive. Here it is of utmost danger to start chemotherapy on
this patient when he actually does not have cancer. In the absence of cancerous cell, chemotherapy will
do certain damage to his normal healthy cells and might lead to severe diseases, even cancer.
Example 2: Let’s say an e-commerce company decided to give $1000 Gift voucher to the customers whom
they assume to purchase at least $10,000 worth of items. They send free voucher mail directly to 100
customers without any minimum purchase condition because they assume to make at least 20% profit on
sold items above $10,000. Now the issue is if we send the $1000 gift vouchers to customers who have not
actually purchased anything but are marked as having made $10,000 worth of purchase.
Q9. Can you cite some examples where a false negative important than a false
positive? And vice versa?
Example 1 FN: What if Jury or judge decides to make a criminal go free?
Example 2 FN: Fraud detection.
Example 3 FP: customer voucher use promo evaluation: if many used it and actually if was not true,
promo sucks.
Q10. Can you cite some examples where both false positive and false negatives
are equally important?
In the Banking industry giving loans is the primary source of making money but at the same time if your
repayment rate is not good you will not make any profit, rather you will risk huge losses.
Banks don’t want to lose good customers and at the same point in time, they don’t want to acquire bad
customers. In this scenario, both the false positives and false negatives become very important to measure.
Q11. Can you explain the difference between a Validation Set and a Test Set?
A Training Set:
• to fit the parameters i.e. weights
A Validation set:
• part of the training set
• for parameter selection
• to avoid overfitting
A Test set:
• for testing or evaluating the performance of a trained machine learning model, i.e. evaluating the
predictive power and generalization.
Q12. Explain cross-validation.
https://machinelearningmastery.com/k-fold-cross-validation/
Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data
sample. The procedure has a single parameter called k that refers to the number of groups that a given
data sample is to be split into. As such, the procedure is often called k-fold cross-validation. When a
specific value for k is chosen, it may be used in place of k in the reference to the model, such as k=10
becoming 10-fold cross-validation. Mainly used in backgrounds where the objective is forecast, and one
wants to estimate how accurately a model will accomplish in practice.
Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning
model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to
perform in general when used to make predictions on data not used during the training of the model.
It is a popular method because it is simple to understand and because it generally results in a less biased
or less optimistic estimate of the model skill than other methods, such as a simple train/test split.
The general procedure is as follows:
1. Shuffle the dataset randomly.
2. Split the dataset into k groups
3. For each unique group:
a. Take the group as a hold out or test data set
b. Take the remaining groups as a training data set
c. Fit a model on the training set and evaluate it on the test set
d. Retain the evaluation score and discard the model
4. Summarize the skill of the model using the sample of model evaluation scores
There is an alternative in Scikit-Learn called Stratified k fold, in which the split is shuffled to make it sure
you have a representative sample of each class and a k fold in which you may not have the assurance of
it (not good with a very unbalanced dataset).
Machine Learning
Q1. What is Machine Learning?
Machine learning is the study of computer algorithms that improve automatically through experience. It
is seen as a subset of artificial intelligence. Machine Learning explores the study and construction of
algorithms that can learn from and make predictions on data. You select a model to train and then
manually perform feature extraction. Used to devise complex models and algorithms that lend themselves
to a prediction which in commercial use is known as predictive analytics.
Q2. What is Supervised Learning?
Supervised learning is the machine learning task of inferring a function from labeled training data. The
training data consist of a set of training examples.
Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor
Algorithm and Neural Networks
E.g. If you built a fruit classifier, the labels will be “this is an orange, this is an apple and this is a banana”,
based on showing the classifier examples of apples, oranges and bananas.
Q3. What is Unsupervised learning?
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets
consisting of input data without labelled responses.
Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent Variable Models
E.g. In the same example, a fruit clustering will categorize as “fruits with soft skin and lots of dimples”,
“fruits with shiny hard skin” and “elongated yellow fruits”.
Q4. What are the various algorithms?
There are various algorithms. Here is a list.
Q5. What is ‘Naive’ in a Naive Bayes?
https://en.wikipedia.org/wiki/Naive_Bayes_classifier
Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with
the “naive” assumption of conditional independence between every pair of features given the value of
the class variable. Bayes’ theorem states the following relationship, given class variable y and dependent
feature vector
through
:
Using the naive conditional independence assumption that each
is independent:
for all , this relationship is simplified to:
Since (
,…,
) is constant given the input, we can use the following classification rule:
and we can use Maximum A Posteriori (MAP) estimation to estimate () and (|
); the former is then
the relative frequency of class in the training set.
The different naive Bayes classifiers differ mainly by the assumptions they make regarding the distribution
of (|
): can be Bernoulli, Binomial, Gaussian, and so on.
Q6. What is PCA? When do you use it?
https://en.wikipedia.org/wiki/Principal_component_analysis
https://blog.umetrics.com/what-is-principal-component-analysis-pca-and-how-it-is-used
https://blog.umetrics.com/why-preprocesing-data-creates-better-data-analytics-models
Principal component analysis (PCA) is a statistical method used in Machine Learning. It consists in
projecting data in a higher dimensional space into a lower dimensional space by maximizing
the variance of each dimension.
The process works as following. We define a matrix A with rows (the single observations of a dataset –
in a tabular format, each single row) and columns, our features. For this matrix we construct a variable
space with as many dimensions as there are features. Each feature represents one coordinate axis. For
each feature, the length has been standardized according to a scaling criterion, normally by scaling to unit
variance. It is determinant to scale the features to a common scale, otherwise the features with a greater
magnitude will weigh more in determining the principal components. Once plotted all the observations
and computed the mean of each variable, that mean will be represented by a point in the center of our
plot (the center of gravity). Then, we subtract each observation with the mean, shifting the coordinate
system with the center in the origin. The best fitting line resulting is the line that best accounts for the
shape of the point swarm. It represents the maximum variance direction in the data. Each observation
may be projected onto this line in order to get a coordinate value along the PC-line. This value is known
as a score. The next best-fitting line can be similarly chosen from directions perpendicular to the first.
Repeating this process yields an orthogonal basis in which different individual dimensions of the data are
uncorrelated. These basis vectors are called principal components.
PCA is mostly used as a tool in exploratory data analysis and for making predictive models. It is often used
to visualize genetic distance and relatedness between populations.
Q7. Explain SVM algorithm in detail.
https://en.wikipedia.org/wiki/Support_vector_machine
Classifying data is a common task in machine learning. Suppose some given data points each belong to
one of two classes, and the goal is to decide which class a new data point will be in. In the case of support-
vector machines, a data point is viewed as a p-dimensional vector (a list of numbers), and we want to
know whether we can separate such points with a ( − 1)-dimensional hyperplane. This is called a linear
classifier. There are many hyperplanes that might classify the data. One reasonable choice as the best
hyperplane is the one that represents the largest separation, or margin, between the two classes. So, we
choose the hyperplane so that the distance from it to the nearest data point on each side is maximized. If
such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines
is known as a maximum-margin classifier; or equivalently, the perceptron of optimal stability. The best
hyper plane that divides the data is
.
We have n data (
,
), … ,
(
,
)
and p different features
= (
, … ,
) and
is either 1 or -1.
The equation of the hyperplane
is as the set of points x satisfying:
∙ − = 0
where is the (not necessarily normalized) normal vector to the hyperplane. The parameter
‖
‖
determines the offset of the hyperplane from the origin along the normal vector w.
So, for each , either
is in the hyperplane of 1 or -1. Basically,
satisfies:
∙
− ≥ 1 ∙
− ≤ −1
• SVMs are helpful in text and hypertext categorization, as their application can significantly reduce
the need for labeled training instances in both the standard inductive and transductive settings.
Some methods for shallow semantic parsing are based on support vector machines.
• Classification of images can also be performed using SVMs. Experimental results show that SVMs
achieve significantly higher search accuracy than traditional query refinement schemes after just
three to four rounds of relevance feedback.
• Classification of satellite data like SAR data using supervised SVM.
• Hand-written characters can be recognized using SVM.
Q8. What are the support vectors in
SVM?
In the diagram, we see that the sketched lines mark the
distance from the classifier (the hyper plane) to the closest
data points called the support vectors (darkened data
points). The distance between the two thin lines is called the
margin.
To extend SVM to cases in which the data are not linearly
separable, we introduce the hinge loss function,
max (0, 1 −
(
∙
−
)
)
This function is zero if x lies on the correct side of the margin. For data on the wrong side of the margin,
the function's value is proportional to the distance from the margin.
Q9. What are the different kernels in SVM?
There are four types of kernels in SVM.
1. LinearKernel
2. Polynomial kernel
3. Radial basis kernel
4. Sigmoid kernel
Q10. What are the most known ensemble algorithms?
https://towardsdatascience.com/the-ultimate-guide-to-adaboost-random-forests-and-xgboost-7f9327061c4f
The most popular trees are: AdaBoost, Random Forest, and eXtreme Gradient Boosting (XGBoost).
AdaBoost is best used in a dataset with low noise, when computational complexity or timeliness of results
is not a main concern and when there are not enough resources for broader hyperparameter tuning due
to lack of time and knowledge of the user.
Random forests should not be used when dealing with time series data or any other data where look-
ahead bias should be avoided, and the order and continuity of the samples need to be ensured. This
algorithm can handle noise relatively well, but more knowledge from the user is required to adequately
tune the algorithm compared to AdaBoost.
The main advantages of XGBoost is its lightning speed compared to other algorithms, such as AdaBoost,
and its regularization parameter that successfully reduces variance. But even aside from the regularization
parameter, this algorithm leverages a learning rate (shrinkage) and subsamples from the features like
random forests, which increases its ability to generalize even further. However, XGBoost is more difficult
to understand, visualize and to tune compared to AdaBoost and random forests. There is a multitude of
hyperparameters that can be tuned to increase performance.
Q11. Explain Decision Tree algorithm in detail.
https://en.wikipedia.org/wiki/Decision_tree_learning
https://www.kdnuggets.com/2019/02/decision-trees-introduction.html/2
https://medium.com/@naeemsunesara/giniscore-entropy-and-information-gain-in-decision-trees-
cbc08589852d
A decision tree is a supervised machine learning algorithm mainly used for Regression and Classification.
It breaks down a data set into smaller and smaller subsets while at the same time an associated decision
tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision
tree can handle both categorical and numerical data. The term Classification and Regression Tree (CART)
analysis is an umbrella term used to refer to both of the above procedures.
Some techniques, often called ensemble methods, construct more than one decision tree:
• Boosted trees Incrementally building an ensemble by training each new instance to emphasize
the training instances previously mis-modeled. A typical example is AdaBoost. These can be used
for regression-type and classification-type problems.
• Bootstrap aggregated (or bagged) decision trees, an early ensemble method, builds multiple
decision trees by repeatedly resampling training data with replacement, and voting the trees for
a consensus prediction.
o A random forest classifier is a specific type of bootstrap aggregating.
• Rotation forest – in which every decision tree is trained by first applying principal component
analysis (PCA) on a random subset of the input features.
A special case of a decision tree is a decision list, which is a one-sided decision tree, so that every internal
node has exactly 1 leaf node and exactly 1 internal node as a child (except for the bottommost node,
whose only child is a single leaf node). While less expressive, decision lists are arguably easier to
understand than general decision trees due to their added sparsity, permit non-greedy learning methods
and monotonic constraints to be imposed.
Notable decision tree algorithms include:
• ID3 (Iterative Dichotomiser 3)
• C4.5 (successor of ID3)
• CART (Classification and Regression Tree)
• Chi-square automatic interaction detection (CHAID). Performs multi-level splits when computing
classification trees.
• MARS: extends decision trees to handle numerical data better.
• Conditional Inference Trees. Statistics-based approach that uses non-parametric tests as splitting
criteria, corrected for multiple testing to avoid overfitting. This approach results in unbiased
predictor selection and does not require pruning.
Q12. What are Entropy and Information gain in Decision tree algorithm?
https://www.saedsayad.com/decision_tree.htm
https://medium.com/@naeemsunesara/giniscore-entropy-and-information-gain-in-decision-trees-
cbc08589852d
There are a lot of algorithms which are employed to build a decision tree, ID3 (Iterative Dichotomiser 3),
C4.5, C5.0, CART (Classification and Regression Trees) to name a few but at their core all of them tell us
what questions to ask and when.
The below table has color and diameter of a fruit and the label tells the name of the fruit. How do we build
a decision tree to classify the fruits?
Here is how we will build the tree. We will start with a node which will ask a true or false question to split
the data into two. The two resulting nodes will each ask a true or false question again to split the data
further and so on.
There are 2 main things to consider with the above approach:
• Which is the best question to ask at each node
• When do we stop splitting the data further?
Let’s start building the tree with the first or the topmost node. There is a list of possible questions which
can be asked. The first node can ask the following questions:
• Is the color green?
• Is the color yellow?
• Is the color red?
• Is the diameter ≥ 3?
• Is the diameter ≥ 1?
Of these possible set of questions, which one is the best to ask so that our data is split into two sets after
the first node? Remember we are trying to split or classify our data into separate classes. Our question
should be such that our data is partitioned into as unmixed or pure classes as possible. An impure set or
class here refers to one which has many different types of objects for example if we ask the question for
the above data, “Is the color green?” our data will be split into two sets one of which will be pure the other
will have a mixed set of labels. If we assign a label to a mixed set, we have higher chances of being
incorrect. But how do we measure this impurity?
Gini Impurity and Information Gain - CART
CART (Classification and Regression Trees) → uses Gini Index (Classification) as metric.
The Gini Impurity (GI) metric measures the homogeneity of a set of items. The lowest possible value of GI
is 0.0. The maximum value of GI depends on the particular problem being investigated but gets close to
1.0.
Suppose for example you have 12 items — apples, grapes, lemons. If there are 0 apples, 0 grapes, 12 lemons,
then you have minimal impurity (this is good for decision trees) and GI = 0.0. But if you have 4 apples, 4
grapes, 4 lemons, you have maximum impurity and it turns out that GI = 0.667.
I’ll show example calculations.
Maximum GI: Apples, Grapes, Lemons
When the number of items is evenly distributed, as in the example above, you have maximum GI but the
exact value depends on how many items there are. A bit less than maximum GI:
In the example above, the items are not quite evenly distributed, and the GI is slightly less (which is better
when used for decision trees). Minimum GI:
The Gini index is not at all the same as a different metric called the Gini coefficient. The Gini impurity
metric can be used when creating a decision tree but there are alternatives, including Entropy
Information gain. The advantage of GI is its simplicity.
Information Gain
Information gain is another metric which tells us how much a question unmixes the labels at a
node. “Mathematically it is just a difference between impurity values before splitting the data at a node
and the weighted average of the impurity after the split”. For instance, if we go back to our data of
apples, lemons and grapes and ask the question “Is the color Green?”
The information gain by asking this question is 0.144. Similarly, we can ask another question from the set
of possible questions split the data and compute information gain. This is also called (Recursive Binary
Splitting).
The question where we have the highest information gain “Is diameter ≥ 3?” is the best question to ask.
Note that the information gain is same for the question “Is the color red?” we just picked the first one at
random.
Repeating the same method at the child node we can complete the tree. Note that no further questions
can be asked which would increase the information gain.
Also note that the rightmost leaf which says 50% Apple & 50% lemon means that this class cannot be
divided further, and this branch can tell an apple or a lemon with 50% probability. For the grape and apple
branches we stop asking further questions since the Gini Impurity is 0 for those.
Entropy and Information Gain – ID3
ID3 (Iterative Dichotomiser 3) → uses Entropy function and Information gain as metrics.
If the sample is completely homogeneous the entropy is zero and if the sample is an equally divided it has
entropy of one.
To build a decision tree, we need to calculate two types of entropy using frequency tables as follows:
a) Entropy using the frequency table of one attribute:
b) Entropy using the frequency table of two attributes:
Information Gain
The information gain is based on the decrease in entropy after a dataset is split on an attribute.
Constructing a decision tree is all about finding attribute that returns the highest information gain (i.e.,
the most homogeneous branches).
Step 1: Calculate entropy of the target.
Step 2: The dataset is then split on the different attributes. The entropy for each branch is calculated.
Then it is added proportionally, to get total entropy for the split. The resulting entropy is subtracted from
the entropy before the split. The result is the Information Gain or decrease in entropy.
Step 3: Choose attribute with the largest information gain as the decision node, divide the dataset by its
branches and repeat the same process on every branch.
Step 4a: A branch with entropy of 0 is a leaf node.
Step 4b: A branch with entropy more than 0 needs further splitting.
Step 5: The ID3 algorithm is run recursively on the non-leaf branches, until all data is classified.
Q13. What is pruning in Decision Tree?
Pruning is a technique in machine learning and search algorithms that reduces the size of decision trees
by removing sections of the tree that provide little power to classify instances. So, when we remove sub-
nodes of a decision node, this process is called pruning or opposite process of splitting.
Q14. What is logistic regression? State an example when you have used logistic
regression recently.
Logistic Regression often referred to as the logit model is a technique to predict the binary outcome from
a linear combination of predictor variables. Since we are interested in a probability outcome, a line does
not fit the model. Logistic Regression is a classification algorithm that works by trying to learn a function
that approximates (|) . It makes the central assumption that (|) can be approximated as a
sigmoid function applied to a linear combination of input features.
For example, if you want to predict whether a particular political leader will win the election or not. In this
case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the
amount of money spent for election campaigning of a particular candidate, the amount of time spent in
campaigning, etc.
Q15. What is Linear Regression?
Linear regression is a statistical technique where the score of a variable Y is predicted from the score of a
second variable X. X is referred to as the predictor variable and Y as the criterion variable.
Q16. What Are the Drawbacks of the Linear Model?
Some drawbacks of the linear model are:
• The assumption of linearity of the model
• It can’t be used for count outcomes or binary outcomes.
• There are overfitting or underfitting problems that it can’t solve.
Q17. What is the difference between Regression and classification ML
techniques?
Both Regression and classification machine learning techniques come under Supervised machine learning
algorithms. In Supervised machine learning algorithm, we have to train the model using labelled data set,
while training we have to explicitly provide the correct labels and algorithm tries to learn the pattern from
input to output. If our labels are discrete values then it will a classification problem, but if our labels are
continuous values then it will be a regression problem.
Q18. What are Recommender Systems?
https://en.wikipedia.org/wiki/Recommender_system
Recommender Systems are a subclass of information filtering systems that are meant to predict the
preferences or ratings that a user would give to a product. Recommender systems are widely used in
movies, news, research articles, products, social tags, music, etc.
Examples include movie recommenders in IMDB, Netflix & BookMyShow, product recommenders in e-
commerce sites like Amazon, eBay & Flipkart, YouTube video recommendations and game
recommendations in Xbox.
Q19. What is Collaborative filtering? And a content based?
The process of filtering used by most of the recommender systems to find patterns or information by
collaborating viewpoints, various data sources and multiple agents. Collaborative filtering is a technique
that can filter out items that a user might like on the basis of reactions by similar users. It works by
searching a large group of people and finding a smaller set of users with tastes similar to a particular user.
It looks at the items they like (usually based on rating) and combines them to create a ranked list of
suggestions. Similar users are those with similar rating and on the based on that they get
recommendations. In content based, we look only at the item level, recommending on similar items sold.
An example of collaborative filtering can be to predict the rating of a particular user based on his/her
ratings for other movies and others’ ratings for all movies. This concept is widely used in recommending
movies in IMDB, Netflix & BookMyShow, product recommenders in e-commerce sites like Amazon, eBay &
Flipkart, YouTube video recommendations and game recommendations in Xbox.
Q20. How can outlier values be treated?
Outlier values can be identified by using univariate or any other graphical analysis method. If the number
of outlier values is few then they can be assessed individually but for a large number of outliers, the values
can be substituted with either the 99th or the 1st percentile values.
All extreme values are not outlier values. The most common ways to treat outlier values:
1. Change it with a mean or median
2. Standardize the feature, changing the distribution but smoothing the outliers
3. Log transform the feature (with many outliers)
4. Drop the value
5. First/third quartile value if more than 2
Q21. What are the various steps involved in an analytics project?
The following are the various steps involved in an analytics project:
1. Understand the Business problem
2. Explore the data and become familiar with it
3. Prepare the data for modeling by detecting outliers, treating missing values, transforming
variables, etc.
4. After data preparation, start running the model, analyze the result and tweak the approach. This
is an iterative step until the best possible outcome is achieved.
5. Validate the model using a new data set.
6. Start implementing the model and track the result to analyze the performance of the model over
the period of time.
Q22. During analysis, how do you treat missing values?
The extent of the missing values is identified after identifying the variables with missing values. If any
patterns are identified the analyst has to concentrate on them as it could lead to interesting and
meaningful business insights.
If there are no patterns identified, then the missing values can be substituted with mean or median values
(imputation) or they can simply be ignored. Assigning a default value which can be mean, minimum or
maximum value. Getting into the data is important.
If it is a categorical variable, the default value is assigned. The missing value is assigned a default value. If
you have a distribution of data coming, for normal distribution give the mean value.
If 80% of the values for a variable are missing, then you can answer that you would be dropping the
variable instead of treating the missing values.
Q23. How will you define the number of clusters in a clustering algorithm?
https://stackabuse.com/hierarchical-clustering-with-python-and-scikit-learn/
Though the Clustering Algorithm is not specified, this question is mostly in reference to K-Means clustering
where “K” defines the number of clusters. The objective of clustering is to group similar entities in a way
that the entities within a group are similar to each other, but the groups are different from each other.
For example, the following image shows three different groups.
Within Sum of squares is generally used to explain the homogeneity within a cluster. If you plot WSS (as
the sum of the squared distance between each member of the cluster and its centroid) for a range of
number of clusters, you will get the plot shown below.
• The Graph is generally known as Elbow Curve.
• Red circled a point in above graph i.e. Number of Cluster = 3 is the point after which you don’t see
any decrement in WSS.
• This point is known as the bending point and taken as K in K – Means.
This is the widely used approach but few data scientists also use Hierarchical clustering first to create
dendrograms and identify the distinct groups from there.
The algorithm starts by finding the two points that are closest to each other on the basis of Euclidean
distance. If we look back at Graph1, we can see that points 2 and 3 are closest to each other while points
7 and 8 are closes to each other. Therefore a cluster will be formed between these two points first. In
Graph2, you can see that the dendograms have been created joining points 2 with 3, and 8 with 7. The
vertical height of the dendogram shows the Euclidean distances between points. From Graph2, it can be
seen that Euclidean distance between points 8 and 7 is greater than the distance between point 2 and 3.
The next step is to join the cluster formed by joining two points to the next nearest cluster or point which
in turn results in another cluster. If you look at Graph1, point 4 is closest to cluster of point 2 and 3,
therefore in Graph2 dendrogram is generated by joining point 4 with dendrogram of point 2 and 3. This
process continues until all the points are joined together to form one big cluster.
Once one big cluster is formed, the longest vertical distance without any horizontal line passing through
it is selected and a horizontal line is drawn through it. The number of vertical lines this newly created
horizontal line passes is equal to number of clusters. Take a look at the following plot:
We can see that the largest vertical distance without any horizontal line passing through it is represented
by blue line. So we draw a new horizontal red line that passes through the blue line. Since it crosses the
blue line at two points, therefore the number of clusters will be 2. Basically the horizontal line is a
threshold, which defines the minimum distance required to be a separate cluster. If we draw a line further
down, the threshold required to be a new cluster will be decreased and more clusters will be formed as
see in the image below:
In the above plot, the horizontal line passes through four vertical lines resulting in four clusters: cluster
of points 6,7,8 and 10, cluster of points 3,2,4 and points 9 and 5 will be treated as single point clusters.
Q24. What is Ensemble Learning?
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain
better predictive performance than could be obtained from any of the constituent learning algorithms
alone. Ensembles are a divide-and-conquer approach used to improve performance. The main principle
behind ensemble methods is that a group of “weak learners” can come together to form a “strong
learner”. Each classifier, individually, is a “weak learner,” while all the classifiers taken together are a
“strong learner”.
Q25. Describe in brief any type of Ensemble Learning.
https://medium.com/@ruhi3929/bagging-and-boosting-method-c036236376eb
Ensemble learning has many types but two more popular ensemble learning techniques are mentioned
below.
Bagging
Bagging tries to implement similar learners on small sample populations and then takes a mean of all the
predictions. In generalized bagging, you can use different learners on different population. As you expect
this helps us to reduce the variance error.
Pros
Ø Bagging method helps when we face variance or overfitting in the model. It provides an
environment to deal with variance by using N learners of same size on same algorithm.
Ø During the sampling of train data, there are many observations which overlaps. So, the
combination of these learners helps in overcoming the high variance.
Ø Bagging uses Bootstrap sampling method (Bootstrapping is any test or metric that uses
random sampling with replacement and falls under the broader class of resampling methods.)
Cons
Ø Bagging is not helpful in case of bias or underfitting in the data.
Ø Bagging ignores the value with the highest and the lowest result which may have a wide difference
and provides an average result.
Boosting
Boosting is an iterative technique which adjusts the weight of an observation based on the last
classification. If an observation was classified incorrectly, it tries to increase the weight of this observation
and vice versa. Boosting in general decreases the bias error and builds strong predictive models. However,
they may over fit on the training data.
Pros
Ø Boosting technique takes care of the weightage of the higher accuracy sample and lower accuracy
sample and then gives the combined results.
Ø Net error is evaluated in each learning steps. It works good with interactions.
Ø Boosting technique helps when we are dealing with bias or underfitting in the data set.
Ø Multiple boosting techniques are available. For example: AdaBoost, LPBoost, XGBoost,
GradientBoost, BrownBoost
Cons
Ø Boosting technique often ignores overfitting or variance issues in the data set.
Ø It increases the complexity of the classification.
Ø Time and computation can be a bit expensive.
There are multiple areas where Bagging and Boosting technique is used to boost the accuracy.
• Banking: Loan defaulter prediction, fraud transaction
• Credit risks
• Kaggle competitions
• Fraud detection
• Recommender system for Netflix
• Malware
• Wildlife conservations and so on.
Q26. What is a Random Forest? How does it work?
Random forest is a versatile machine learning method capable of performing:
• regression
• classification
• dimensionality reduction
• treat missing values
• outlier values
It is a type of ensemble learning method, where a group of weak models combine to form a powerful
model. The random forest starts with a standard machine learning technique called a “decision tree”
which, in ensemble terms, corresponds to our weak learner. In a decision tree, an input is entered at the
top and as it traverses down the tree the data gets bucketed into smaller and smaller sets.
In Random Forest, we grow multiple trees as opposed to a single tree. To classify a new object based on
attributes, each tree gives a classification. The forest chooses the classification having the most votes
(Over all the trees in the forest) and in case of regression, it takes the average of outputs by different
trees.
Q27. How Do You Work Towards a Random Forest?
https://blog.citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics
The underlying principle of this technique is that several weak learners combined to provide a keen
learner. Here is how such a system is trained for some number of trees T:
1. Sample N cases at random with replacement to create a subset of the data. The subset should be
about 66% of the total set.
2. At each node:
a. For some number m (see below), m predictor variables are selected at random from all
the predictor variables.
b. The predictor variable that provides the best split, according to some objective function,
is used to do a binary split on that node.
c. At the next node, choose another m variables at random from all predictor variables and
do the same.
Depending upon the value of m, there are three slightly different systems:
• Random splitter selection: = 1
• Breiman’s bagger: = total number of predictor variables ()
• Random forest: ≪ number of predictor variables.
o Brieman suggests three possible values for :
,
, 2
When a new input is entered into the system, it is run down all of the trees. The result may either be an
average or weighted average of all of the terminal nodes that are reached, or, in the case of categorical
variables, a voting majority.
Note that:
Ø With a large number of predictors ( ≫ 0), the eligible predictor set () will be quite different
from node to node.
Ø The greater the inter-tree correlation, the greater the random forest error rate, so one pressure
on the model is to have the trees as uncorrelated as possible.
Ø As m goes down, both inter-tree correlation and the strength of individual trees go down. So some
optimal value of m must be discovered.
Ø Strengths: Random forest runtimes are quite fast, and they are able to deal with unbalanced and
missing data.
Ø Weaknesses: Random Forest used for regression cannot predict beyond the range in the training
data, and that they may over-fit data sets that are particularly noisy. Of course, the best test of
any algorithm is how well it works upon your own data set.
Q28. What cross-validation technique would you use on a time series data set?
Instead of using k-fold cross-validation, you should be aware of the fact that a time series is not randomly
distributed data — It is inherently ordered by chronological order.
In case of time series data, you should use techniques like forward=chaining — Where you will be model
on past data then look at forward-facing data.
fold 1: training[1], test[2]
fold 2: training[1 2], test[3]
fold 3: training[1 2 3], test[4]
fold 4: training[1 2 3 4], test[5]
Q29. What is a Box-Cox Transformation?
The dependent variable for a regression analysis might not satisfy one or more assumptions of an ordinary
least squares regression. The residuals could either curve as the prediction increases or follow the skewed
distribution. In such scenarios, it is necessary to transform the response variable so that the data meets
the required assumptions. A Box-Cox transformation is a statistical technique to transform non-normal
dependent variables into a normal shape. If the given data is not normal then most of the statistical
techniques assume normality. Applying a Box-Cox transformation means that you can run a broader
number of tests.
A Box-Cox transformation is a way to transform non-normal dependent variables into a normal shape.
Normality is an important assumption for many statistical techniques, if your data isn’t normal, applying
a Box-Cox means that you are able to run a broader number of tests. The Box-Cox transformation is named
after statisticians George Box and Sir David Roxbee Cox who collaborated on a 1964 paper and developed
the technique.
Q30. How Regularly Must an Algorithm be Updated?
You will want to update an algorithm when:
• You want the model to evolve as data streams through infrastructure
• The underlying data source is changing
• There is a case of non-stationarity (mean, variance change over the time)
• The algorithm underperforms/results lack accuracy
Q31. If you are having 4GB RAM in your machine and you want to train your model
on 10GB data set. How would you go about this problem? Have you ever faced this
kind of problem in your machine learning/data science experience so far?
First of all, you have to ask which ML model you want to train.
For Neural networks: Batch size with Numpy array will work. Steps:
1. Load the whole data in the Numpy array. Numpy array has a property to create a mapping of the
complete data set, it doesn’t load complete data set in memory.
2. You can pass an index to Numpy array to get required data.
3. Use this data to pass to the Neural network.
4. Have a small batch size.
For SVM: Partial fit will work. Steps:
1. Divide one big data set in small size data sets.
2. Use a partial fit method of SVM, it requires a subset of the complete data set.
3. Repeat step 2 for other subsets.
However, you could actually face such an issue in reality. So, you could check out the best laptop for
Machine Learning to prevent that. Having said that, let’s move on to some questions on deep learning.
Deep Learning
Q1. What do you mean by Deep Learning?
Deep Learning is nothing but a paradigm of machine learning which has shown incredible promise in
recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of
the neurons in the human brain.
Q2. What is the difference between machine learning and deep learning?
https://parsers.me/deep-learning-machine-learning-whats-the-difference/
Machine learning is a field of computer science that gives computers the ability to learn without being
explicitly programmed. Machine learning can be categorized in the following four categories.
1. Supervised machine learning,
2. Semi-supervised machine learning,
3. Unsupervised machine learning,
4. Reinforcement learning.
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and
function of the brain called artificial neural networks.
• The main difference between deep learning and machine learning is due to the way data is
presented in the system. Machine learning algorithms almost always require structured data,
while deep learning networks rely on layers of ANN (artificial neural networks).
• Machine learning algorithms are designed to “learn” to act by understanding labeled data and
then use it to produce new results with more datasets. However, when the result is incorrect,
there is a need to “teach them”. Because machine learning algorithms require bulleted data, they
are not suitable for solving complex queries that involve a huge amount of data.
• Deep learning networks do not require human intervention, as multilevel layers in neural
networks place data in a hierarchy of different concepts, which ultimately learn from their own
mistakes. However, even they can be wrong if the data quality is not good enough.
• Data decides everything. It is the quality of the data that ultimately determines the quality of the
result.
• Both of these subsets of AI are somehow connected to data, which makes it possible to represent
a certain form of “intelligence.” However, you should be aware that deep learning requires much
more data than a traditional machine learning algorithm. The reason for this is that deep learning
networks can identify different elements in neural network layers only when more than a million
data points interact. Machine learning algorithms, on the other hand, are capable of learning by
pre-programmed criteria.
Q3. What, in your opinion, is the reason for the popularity of Deep Learning in
recent times?
Now although Deep Learning has been around for many years, the major breakthroughs from these
techniques came just in recent years. This is because of two main reasons:
• The increase in the amount of data generated through various sources
• The growth in hardware resources required to run these models
GPUs are multiple times faster and they help us build bigger and deeper deep learning models in
comparatively less time than we required previously.
Q4. What is reinforcement learning?
Reinforcement Learning allows to take actions to max cumulative reward. It learns by trial and error
through reward/penalty system. Environment rewards agent so by time agent makes better decisions.
Ex: robot=agent, maze=environment. Used for complex tasks (self-driving cars, game AI).
RL is a series of time steps in a Markov Decision Process:
1. Environment: space in which RL operates
2. State: data related to past action RL took
3. Action: action taken
4. Reward: number taken by agent after last action
5. Observation: data related to environment: can be visible or partially shadowed
Q5. What are Artificial Neural Networks?
Artificial Neural networks are a specific set of algorithms that have revolutionized machine learning. They
are inspired by biological neural networks. Neural Networks can adapt to changing the input, so the
network generates the best possible result without needing to redesign the output criteria.
Q6. Describe the structure of Artificial Neural Networks?
Artificial Neural Networks works on the same principle as a biological Neural Network. It consists of inputs
which get processed with weighted sums and Bias, with the help of Activation Functions.
Q7. How Are Weights Initialized in a Network?
There are two methods here: we can either initialize the weights to zero or assign them randomly.
Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every
layer perform the same operation, giving the same output and making the deep net useless.
Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close
to 0. It gives better accuracy to the model since every neuron performs different computations. This is the
most commonly used method.
Q8. What Is the Cost Function?
Also referred to as “loss” or “error,” cost function is a measure to evaluate how good your model’s
performance is. It’s used to compute the error of the output layer during backpropagation. We push that
error backwards through the neural network and use that during the different training functions.
The most known one is the mean sum of squared errors.
=
(
∑
(
)
+
)
Q9. What Are Hyperparameters?
With neural networks, you’re usually working with hyperparameters once the data is formatted correctly.
A hyperparameter is a parameter whose value is set before the learning process begins. It determines
how a network is trained and the structure of the network (such as the number of hidden units, the
learning rate, epochs, batches, etc.).
Q10. What Will Happen If the Learning Rate Is Set inaccurately (Too Low or Too
High)?
When your learning rate is too low, training of the model will progress very slowly as we are making
minimal updates to the weights. It will take many updates before reaching the minimum point.
If the learning rate is set too high, this causes undesirable divergent behavior to the loss function due to
drastic updates in weights. It may fail to converge (model can give a good output) or even diverge (data is
too chaotic for the network to train).
Q11. What Is The Difference Between Epoch, Batch, and Iteration in Deep
Learning?
• Epoch – Represents one iteration over the entire dataset (everything put into the training model).
• Batch – Refers to when we cannot pass the entire dataset into the neural network at once, so we
divide the dataset into several batches.
• Iteration – if we have 10,000 images as data and a batch size of 200. then an epoch should run 50
iterations (10,000 divided by 50).
Q12. What Are the Different Layers on CNN?
https://towardsdatascience.com/basics-of-the-classic-cnn-a3dce1225add
The Convolutional neural networks are regularized versions of multilayer perceptron (MLP). They were
developed based on the working of the neurons of the animal visual cortex.
Let’s say we have a color image in JPG form and its size is 480 x 480. The representative array will be 480
x 480 x 3. Each of these numbers is given a value from 0 to 255 which describes the pixel intensity at that
point. RGB intensity values of the image are visualized by the computer for processing.
The objective of using the CNN:
The idea is that you give the computer this array of numbers and it will output numbers that describe the
probability of the image being a certain class (.80 for a cat, .15 for a dog, .05 for a bird, etc.). It works
similar to how our brain works. When we look at a picture of a dog, we can classify it as such if the picture
has identifiable features such as paws or 4 legs. In a similar way, the computer is able to perform image
classification by looking for low-level features such as edges and curves and then building up to more
abstract concepts through a series of convolutional layers. The computer uses low-level features obtained
at the initial levels to generate high-level features such as paws or eyes to identify the object.
There are four layers in CNN:
1. Convolutional Layer – the layer that performs a convolutional operation, creating several smaller
picture windows to go over the data.
2. Activation Layer (ReLU Layer) – it brings non-linearity to the network and converts all the negative
pixels to zero. The output is a rectified feature map. It follows each convolutional layer.
3. Pooling Layer – pooling is a down-sampling operation that reduces the dimensionality of the
feature map. Stride = how much you slide, and you get the max of the matrix
4. Fully Connected Layer – this layer recognizes and classifies the objects in the image.
Convolution Operation
First Layer:
1. Input to a convolutional layer
The image is resized to an optimal size and is fed as input to the convolutional layer.
Let us consider the input as 32x32x3 array of pixel values.
2. There exists a filter or neuron or kernel which lays over some of the pixels of the input image
depending on the dimensions of the Kernel size.
Let the dimensions of the kernel of the filter be 5x5x3.
3. The Kernel actually slides over the input image; thus, it is multiplying the values in the filter
with the original pixel values of the image (aka computing element-wise multiplications).
The multiplications are summed up generating a single number for that particular receptive field
and hence for sliding the kernel a total of 784 numbers are mapped to 28x28 array known as the
feature map.
**Now if we consider two kernels of the same dimension then the obtained first layer feature map
will be (28x28x2).
High-level Perspective
• Let us take a kernel of size (7x7x3) for understanding. Each of the kernels is considered to be a
feature identifier, hence say that our filter will be a curve detector.
• The original image and the visualization of the kernel on the image.
The sum of the multiplication value that is generated is = 4 ∗ (50 ∗ 30) + (20 ∗ 30) = 6600 (large
number).
• Now when the kernel moves to the other part of the image.
The sum of the multiplication value that is generated is = 0 (small number).
The use of the small and the large value
1. The value is much lower! This is because there wasn’t anything in the image section that responded
to the curve detector filter. Remember, the output of this convolution layer is an activation map.
So, in the simple case of a one filter convolution (and if that filter is a curve detector), the activation
map will show the areas in which there at most likely to be curved in the picture.
2. In the previous example, the top-left value of our 26 x 26 x 1 activation map (26 because of the 7x7
filter instead of 5x5) will be 6600. This high value means that it is likely that there is some sort of
curve in the input volume that caused the filter to activate. The top right value in our activation
map will be 0 because there wasn’t anything in the input volume that caused the filter to activate.
This is just for one filter.
3. This is just a filter that is going to detect lines that curve outward and to the right. We can have
other filters for lines that curve to the left or for straight edges. The more filters, the greater the
depth of the activation map, and the more information we have about the input volume.
In the picture, we can see some examples of actual visualizations of the filters of the first conv.
layer of a trained network. Nonetheless, the main argument remains the same. The filters on the
first layer convolve around the input image and “activate” (or compute high values) when the
specific feature it is looking for is in the input volume.
Sequential convolutional layers after the first one
1. When we go through another conv. layer, the output of the first conv. layer becomes the input of
the 2nd conv. layer.
2. However, when we’re talking about the 2nd conv. layer, the input is the activation map(s) that
result from the first layer. So, each layer of the input is basically describing the locations in the
original image for where certain low-level features appear.
3. Now when you apply a set of filters on top of that (pass it through the 2nd conv. layer), the output
will be activations that represent higher-level features. Types of these features could be
semicircles (a combination of a curve and straight edge) or squares (a combination of several
straight edges). As you go through the network and go through more convolutional layers, you
get activation maps that represent more and more complex features.
4. By the end of the network, you may have some filters that activate when there is handwriting in
the image, filters that activate when they see pink objects, etc.
Pooling Operation
It consists in getting the largest number out of a matrix to get the most important number and reduce
the dimention.
Max Pooling example
2x2 filters with stride = 2 (maximum value) is considered
3x3 filters with stride = 1 (maximum value) is considered
3x3 filters with stride = 2 (maximum value) is considered
Classification
1. Flatten: The pooled matrix is converted to a vector.
2. Fully Connected layer: The way this fully connected layer works is that it looks at the output of
the previous layer (which as we remember should represent the activation maps of high-level
features) and the number of classes p (10 for digit classification). For example, if the program is
predicting that some image is a dog, it will have high values in the activation maps that represent
high-level features like a paw or 4 legs, etc. Basically, an FC layer looks at what high level features
most strongly correlate to a particular class and has particular weights so that when you compute
the products between the weights and the previous layer, you get the correct probabilities for the
different classes.
3. Soft-max approach: The output of a fully connected layer is as follows [0 .1 .1 .75 0 0 0 0 0 .05],
then this represents a 10% probability that the image is a 1, a 10% probability that the image is a
2, a 75% probability that the image is a 3, and a 5% probability that the image is a 9 (SoftMax
approach) for digit classification.
Training
§We know kernels also known as feature identifiers, used for identification of specific features. But how
the kernels are initialized with the specific weights or how do the filters know what values to have.
Hence comes the important step of training. The training process is also known as backpropagation, which
is further separated into 4 distinct sections or processes.
• Forward Pass
• Loss Function
• Backward Pass
• Weight Update
The Forward Pass
For the first epoch or iteration of the training the initial kernels of the first convolutional layer are
initialized with random values. Thus, after the first iteration output will be something like
[.1.1.1.1.1.1.1.1.1.1], which does not give preference to any class as the kernels don’t have specific
weights.
The Loss Function
The training involves images along with labels, hence the label for the digit 3 will be [0 0 0 1 0 0 0 0 0 0],
whereas the output after a first epoch is very different, hence we will calculate loss (MSE — Mean Squared
Error)
The objective is to minimize the loss, which is an optimization problem in calculus. It involves trying to
adjust the weights to reduce the loss.
The Backward Pass
It involves determining which weights contributed most to the loss and finding ways to adjust them so
that the loss decreases. It is computed using
(or ∇
), where L is the loss and the W is the weights of
the corresponding kernel.
The weights update
This is where the weights of the kernel are updated using the following equation.
Here the Learning Rate is chosen by the programmer. Larger value of the learning rate indicates much
larger steps towards optimization of steps and larger time to convolve to an optimized weight.
Testing
Finally, to see whether or not our CNN works, we have a different set of images and labels (can’t double
dip between training and test!) and pass the images through the CNN. We compare the outputs to the
ground truth and see if our network works!
Q13. What Is Pooling on CNN, and How Does It Work?
Pooling is used to reduce the spatial dimensions of a CNN. It performs down-sampling operations to
reduce the dimensionality and creates a pooled feature map by sliding a filter matrix over the input matrix.
Q14. What are Recurrent Neural Networks (RNNs)?
https://towardsdatascience.com/recurrent-neural-networks-d4642c9bc7ce
RNNs are a type of artificial neural networks designed to recognize the pattern from the sequence of data
such as Time series, stock market and government agencies etc.
Recurrent Neural Networks (RNNs) add an interesting twist to basic neural networks. A vanilla neural
network takes in a fixed size vector as input which limits its usage in situations that involve a ‘series’ type
input with no predetermined size.
RNNs are designed to take a series of input with no predetermined limit on size. One could ask what’s
the big deal, I can call a regular NN repeatedly too?
Sure can, but the ‘series’ part of the input means something. A single input item from the series is related
to others and likely has an influence on its neighbors. Otherwise it's just “many” inputs, not a “series”
input (duh!).
Recurrent Neural Network remembers the past and its decisions are influenced by what it has learnt from
the past. Note: Basic feed forward networks “remember” things too, but they remember things they
learnt during training. For example, an image classifier learns what a “1” looks like during training and
then uses that knowledge to classify things in production.
While RNNs learn similarly while training, in addition, they remember things learnt from prior input(s)
while generating output(s). RNNs can take one or more input vectors and produce one or more output
vectors and the output(s) are influenced not just by weights applied on inputs like a regular NN, but also
by a “hidden” state vector representing the context based on prior input(s)/output(s). So, the same input
could produce a different output depending on previous inputs in the series.
In summary, in a vanilla neural network, a fixed size input vector is transformed into a fixed size output
vector. Such a network becomes “recurrent” when you repeatedly apply the transformations to a series
of given input and produce a series of output vectors. There is no pre-set limitation to the size of the
vector. And, in addition to generating the output which is a function of the input and hidden state, we
update the hidden state itself based on the input and use it in processing the next input.
Parameter Sharing
You might have noticed another key difference between Figure 1 and Figure 3. In the earlier, multiple
different weights are applied to the different parts of an input item generating a hidden layer neuron,
which in turn is transformed using further weights to produce an output. There seems to be a lot of
weights in play here. Whereas in Figure 3, we seem to be applying the same weights over and over again
to different items in the input series.
I am sure you are quick to point out that we are kind of comparing apples and oranges here. The first
figure deals with “a” single input whereas the second figure represents multiple inputs from a series. But
nevertheless, intuitively speaking, as the number of inputs increase, shouldn’t the number of weights in
play increase as well? Are we losing some versatility and depth in Figure 3?
Perhaps we are. We are sharing parameters across inputs in Figure 3. If we don’t share parameters across
inputs, then it becomes like a vanilla neural network where each input node requires weights of their own.
This introduces the constraint that the length of the input has to be fixed and that makes it impossible to
leverage a series type input where the lengths differ and is not always known.
But what we seemingly lose in value here, we gain back by introducing the “hidden state” that links one
input to the next. The hidden state captures the relationship that neighbors might have with each other
in a serial input and it keeps changing in every step, and thus effectively every input undergoes a different
transition!
Image classifying CNNs have become so successful because the 2D convolutions are an effective form of
parameter sharing where each convolutional filter basically extracts the presence or absence of a feature
in an image which is a function of not just one pixel but also of its surrounding neighbor pixels.
In other words, the success of CNNs and RNNs can be attributed to the concept of “parameter sharing”
which is fundamentally an effective way of leveraging the relationship between one input item and its
surrounding neighbors in a more intrinsic fashion compared to a vanilla neural network.
Deep RNNs
While it’s good that the introduction of hidden state enabled us to effectively identify the relationship
between the inputs, is there a way we can make an RNN “deep” and gain the multi-level abstractions and
representations we gain through “depth” in a typical neural network?
Here are four possible ways to add depth.
1) We can add hidden states, one on top of another, feeding the output of one to the next.
2) We can also add additional nonlinear hidden layers between input to hidden state.
3) We can increase depth in the hidden to hidden transition.
4) We can increase depth in the hidden to output transition.
Bidirectional RNNs
Sometimes it’s not just about learning from the past to predict the future, but we also need to look into
the future to fix the past. In speech recognition and handwriting recognition tasks, where there could be
considerable ambiguity given just one part of the input, we often need to know what’s coming next to
better understand the context and detect the present.
This does introduce the obvious challenge of how much into the future we need to look into, because if
we have to wait to see all inputs then the entire operation will become costly. And in cases like speech
recognition, waiting till an entire sentence is spoken might make for a less compelling use case. Whereas
for NLP tasks, where the inputs tend to be available, we can likely consider entire sentences all at once.
Also, depending on the application, if the sensitivity to immediate and closer neighbors is higher than
inputs that come further away, a variant that looks only into a limited future/past can be modeled.
Recursive Neural Network
A recurrent neural network parses the inputs in a sequential fashion. A recursive neural network is similar
to the extent that the transitions are repeatedly applied to inputs, but not necessarily in a sequential
fashion. Recursive Neural Networks are a more general form of Recurrent Neural Networks. It can operate
on any hierarchical tree structure. Parsing through input nodes, combining child nodes into parent nodes
and combining them with other child/parent nodes to create a tree like structure. Recurrent Neural
Networks do the same, but the structure there is strictly linear. i.e. weights are applied on the first input
node, then the second, third and so on.
But this raises questions pertaining to the structure. How do we decide that? If the structure is fixed like
in Recurrent Neural Networks then the process of training, backprop, makes sense in that they are similar
to a regular neural network. But if the structure isn’t fixed, is that learnt as well?
Encoder Decoder Sequence to Sequence RNNs
Encoder Decoder or Sequence to Sequence RNNs are used a lot in translation services. The basic idea is
that there are two RNNs, one an encoder that keeps updating its hidden state and produces a final single
“Context” output. This is then fed to the decoder, which translates this context to a sequence of outputs.
Another key difference in this arrangement is that the length of the input sequence and the length of the
output sequence need not necessarily be the same.
LSTMs
LSTM is not a different variant of RNN architecture, but rather it introduces changes to how we compute
outputs and hidden state using the inputs.
In a vanilla RNN, the input and the hidden state are simply passed through a single tanh layer. LSTM (Long-
Short-Term Memory) networks improve on this simple transformation and introduces additional gates
and a cell state, such that it fundamentally addresses the problem of keeping or resetting context, across
sentences and regardless of the distance between such context resets. There are variants of LSTMs
including GRUs that utilize the gates in different manners to address the problem of long-term
dependencies.
Q15. How Does an LSTM Network Work?
http://karpathy.github.io/2015/05/21/rnn-effectiveness
https://colah.github.io/posts/2015-08-Understanding-LSTMs/
Long-Short-Term Memory (LSTM) is a special kind of recurrent neural network capable of learning long-
term dependencies, remembering information for long periods as its default behavior. There are three
steps in an LSTM network:
• Step 1: The network decides what to forget and what to remember.
• Step 2: It selectively updates cell state values.
• Step 3: The network decides what part of the current state makes it to the output.
Recurrent Neural Networks
Humans don’t start their thinking from scratch every second. As you read this essay, you understand each
word based on your understanding of previous words. You don’t throw everything away and start thinking
from scratch again. Your thoughts have persistence.
Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine
you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional
neural network could use its reasoning about previous events in the film to inform later ones.
Recurrent neural networks address this issue. They are networks with loops in them, allowing information
to persist.
Recurrent Neural Networks have loops.
In the above diagram, a chunk of neural network, A, looks at some input
and outputs a value ℎ
. A loop
allows information to be passed from one step of the network to the next.
These loops make recurrent neural networks seem kind of mysterious. However, if you think a bit more,
it turns out that they aren’t all that different than a normal neural network. A recurrent neural network
can be thought of as multiple copies of the same network, each passing a message to a successor. Consider
what happens if we unroll the loop:
An unrolled recurrent neural network.
This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists.
They’re the natural architecture of neural network to use for such data.
And they certainly are used! In the last few years, there have been incredible success applying RNNs to a
variety of problems: speech recognition, language modeling, translation, image captioning…
Essential to these successes is the use of “LSTMs,” a very special kind of recurrent neural network which
works, for many tasks, much better than the standard version. Almost all exciting results based on
recurrent neural networks are achieved with them.
The Problem of Long-Term Dependencies
One of the appeals of RNNs is the idea that they might be able to connect previous information to the
present task, such as using previous video frames might inform the understanding of the present frame.
If RNNs could do this, they’d be extremely useful. But can they? It depends.
Sometimes, we only need to look at recent information to perform the present task. For example, consider
a language model trying to predict the next word based on the previous ones. If we are trying to predict
the last word in “the clouds are in the sky,” we don’t need any further context – it’s pretty obvious the next
word is going to be sky. In such cases, where the gap between the relevant information and the place that
it’s needed is small, RNNs can learn to use the past information.
But there are also cases where we need more context. Consider trying to predict the last word in the text
“I grew up in France… I speak fluent French.” Recent information suggests that the next word is probably
the name of a language, but if we want to narrow down which language, we need the context of France,
from further back. It’s entirely possible for the gap between the relevant information and the point where
it is needed to become very large.
Unfortunately, as that gap grows, RNNs become unable to learn to connect the information.
In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could
carefully pick parameters for them to solve toy problems of this form. Sadly, in practice, RNNs don’t seem
to be able to learn them. Thankfully, LSTMs don’t have this problem!
LSTM Networks
Long Short-Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of
learning long-term dependencies. They work tremendously well on a large variety of problems and are
now widely used.
LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for
long periods of time is practically their default behavior, not something they struggle to learn!
All recurrent neural networks have the form of a chain of repeating modules of neural network. In
standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer.
The repeating module in a standard RNN contains a single layer.
LSTMs also have this chain like structure, but the repeating module has a different structure. Instead of
having a single neural network layer, there are four, interacting in a very special way.
The repeating module in an LSTM contains four interacting layers.
In the above diagram, each line carries an entire vector, from the output of one node to the inputs of
others. The pink circles represent pointwise operations, like vector addition, while the yellow boxes are
learned neural network layers. Lines merging denote concatenation, while a line forking denotes its
content being copied and the copies going to different locations.
The Core Idea Behind LSTMs
The key to LSTMs is the cell state, the horizontal line running through the top of the diagram.
The cell state is kind of like a conveyor belt. It runs straight down the entire chain, with only some minor
linear interactions. It’s very easy for information to just flow along it unchanged.
The LSTM does have the ability to remove or add information to the cell state, carefully regulated by
structures called gates.
Gates are a way to optionally let information through. They are composed out of a sigmoid neural net
layer and a pointwise multiplication operation.
The sigmoid layer outputs numbers between zero and one, describing how much of each component
should be let through. A value of zero means “let nothing through,” while a value of one means “let
everything through!”
An LSTM has three of these gates, to protect and control the cell state.
Q16. What Is a Multi-layer Perceptron (MLP)?
https://towardsdatascience.com/what-the-hell-is-perceptron-626217814f53
As in Neural Networks, MLPs have an input layer, a hidden layer, and an output layer. It has the same
structure as a single layer perceptron with one or more hidden layers.
Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks.
A (single layer) perceptron is a single layer neural network that works as a linear binary classifier. Being a
single layer neural network, it can be trained without the use of more advanced algorithms like back
propagation and instead can be trained by "stepping towards" your error in steps specified by a learning
rate. When someone says perceptron, I usually think of the single layer version.
A single layer perceptron can classify only linear separable classes with binary output {0,1} or {-1,1}, but
MLP can classify nonlinear classes. The activation functions are used to map the input between the
required values like {0, 1} or {-1, 1}.
Except for the input layer, each node in the other layers uses a nonlinear activation function. This means
the input layers, the data coming in, and the activation function is based upon all nodes and weights being
added together, producing the output. MLP uses a supervised learning method called “backpropagation.”
In backpropagation, the neural network calculates the error with the help of cost function. It propagates
this error backward from where it came (adjusts the weights to train the model more accurately).
Usually, RELU is in hidden layers (it does not classify), and Soft-max or tanh is in output layers.
Q17. Explain Gradient Descent.
Let’s first explain what a gradient is. A gradient is a mathematical function. When calculated on a point of
a function, it gives the hyperplane (or slope) of the directions in which the function increases more. The
gradient vector can be interpreted as the "direction and rate of fastest increase". If the gradient of a
function is non-zero at a point p, the direction of the gradient is the direction in which the function
increases most quickly from p, and the magnitude of the gradient is the rate of increase in that direction.
Further, the gradient is the zero vector at a point if and only if it is a stationary point (where the derivative
vanishes).
In DS, it simply measures the change in all weights with regard to the change in error, as we are partially
derivating by w the loss function.
Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.
The goal of the gradient descent is to minimize a given function which, in our case, is the loss function of
the neural network. To achieve this goal, it performs two steps iteratively.
1. Compute the slope (gradient) that is the first-order derivative of the function at the current point
2. Move-in the opposite direction of the slope increase from the current point by the computed
amount
So, the idea is to pass the training set through the hidden layers of the neural network and then update
the parameters of the layers by computing the gradients using the training samples from the training
dataset.
Think of it like this. Suppose a man is at top of the valley and he wants to get to the bottom of the valley.
So, he goes down the slope. He decides his next position based on his current position and stops when he
gets to the bottom of the valley which was his goal.
Q18. What is exploding gradients?
https://machinelearningmastery.com/exploding-gradients-in-neural-networks/
While training an RNN, if you see exponentially growing (very large) error gradients which accumulate and
result in very large updates to neural network model weights during training, they’re known as exploding
gradients. At an extreme, the values of weights can become so large as to overflow and result in NaN
values. The explosion occurs through exponential growth by repeatedly multiplying gradients through the
network layers that have values larger than 1.0.
This has the effect of your model is unstable and unable to learn from your training data.
There are some subtle signs that you may be suffering from exploding gradients during the training of
your network, such as:
• The model is unable to get traction on your training data (e.g. poor loss).
• The model is unstable, resulting in large changes in loss from update to update.
• The model loss goes to NaN during training.
• The model weights quickly become very large during training.
• The error gradient values are consistently above 1.0 for each node and layer during training.
Solutions
1. Re-Design the Network Model:
a. In deep neural networks, exploding gradients may be addressed by redesigning the
network to have fewer layers. There may also be some benefit in using a smaller batch
size while training the network.
b. In RNNs, updating across fewer prior time steps during training, called truncated
Backpropagation through time, may reduce the exploding gradient problem.
2. Use Long Short-Term Memory Networks: In RNNs, exploding gradients can be reduced by using
the Long Short-Term Memory (LSTM) memory units and perhaps related gated-type neuron
structures. Adopting LSTM memory units is a new best practice for recurrent neural networks for
sequence prediction.
3. Use Gradient Clipping: Exploding gradients can still occur in very deep Multilayer Perceptron
networks with a large batch size and LSTMs with very long input sequence lengths. If exploding
gradients are still occurring, you can check for and limit the size of gradients during the training
of your network. This is called gradient clipping. Specifically, the values of the error gradient are
checked against a threshold value and clipped or set to that threshold value if the error gradient
exceeds the threshold.
4. Use Weight Regularization: another approach, if exploding gradients are still occurring, is to check
the size of network weights and apply a penalty to the networks loss function for large weight
values. This is called weight regularization and often an L1 (absolute weights) or an L2 (squared
weights) penalty can be used.
Q19. What is vanishing gradients?
While training an RNN, your slope can become either too small; this makes the training difficult. When
the slope is too small, the problem is known as a Vanishing Gradient. It leads to long training times, poor
performance, and low accuracy.
• Hyperbolic tangent and Sigmoid/Soft-max suffer vanishing gradient.
• RNNs suffer vanishing gradient, LSTM no (so it is perfect to predict stock prices). In fact, the
propagation of error through previous layers makes the gradient get smaller so the weights are
not updated.
Solutions
1. Choose RELU
2. Use LSTM (for RNNs)
3. Use ResNet (Residual Network) à after some layers, add x again:
(
)
→ ⋯ →
(
)
+
4. Multi-level hierarchy: pre-train one layer at the time through unsupervised learning, then fine-
tune via backpropagation
5. Gradient checking: debugging strategy used to numerically track and assess gradients during
training.
Q20. What is Back Propagation and Explain it Works.
Backpropagation is a training algorithm used for neural network. In this method, we update the weights
of each layer from the last layer recursively, with the formula:
=
− ∇
(
)
It has the following steps:
• Forward Propagation of Training Data (initializing weights with random or pre-assigned values)
• Gradients are computed using output weights and target
• Back Propagate for computing gradients of error from output activation
• Update the Weights
Q21. What are the variants of Back Propagation?
https://towardsdatascience.com/batch-mini-batch-stochastic-gradient-descent-7a62ecba642a
• Stochastic Gradient Descent: In Batch Gradient Descent we were considering all the examples for
every step of Gradient Descent. But what if our dataset is very huge. Deep learning models crave for
data. The more the data the more chances of a model to be good. Suppose our dataset has 5 million
examples, then just to take one step the model will have to calculate the gradients of all the 5 million
examples. This does not seem an efficient way. To tackle this problem, we have Stochastic Gradient
Descent. In Stochastic Gradient Descent (SGD), we consider just one example at a time to take a single
step. We do the following steps in one epoch for SGD:
1. Take an example
2. Feed it to Neural Network
3. Calculate its gradient
4. Use the gradient we calculated in step 3 to update the weights
5. Repeat steps 1–4 for all the examples in training dataset
Since we are considering just one example at a time the cost will fluctuate over the training examples
and it will not necessarily decrease. But in the long run, you will see the cost decreasing with
fluctuations. Also, because the cost is so fluctuating, it will never reach the minimum, but it will keep
dancing around it. SGD can be used for larger datasets. It converges faster when the dataset is large
as it causes updates to the parameters more frequently.
• Batch Gradient Descent: all the training data is taken into consideration to take a single step. We take
the average of the gradients of all the training examples and then use that mean gradient to update
our parameters. So that’s just one step of gradient descent in one epoch. Batch Gradient Descent is
great for convex or relatively smooth error manifolds. In this case, we move somewhat directly
towards an optimum solution. The graph of cost vs epochs is also quite smooth because we are
averaging over all the gradients of training data for a single step. The cost keeps on decreasing over
the epochs.
• Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of
Stochastic Gradient Descent and here instead of single training example, mini batch of samples is
used. Batch Gradient Descent can be used for smoother curves. SGD can be used when the dataset is
large. Batch Gradient Descent converges directly to minima. SGD converges faster for larger datasets.
But, since in SGD we use only one example at a time, we cannot implement the vectorized
implementation on it. This can slow down the computations. To tackle this problem, a mixture of
Batch Gradient Descent and SGD is used. Neither we use all the dataset all at once nor we use the
single example at a time. We use a batch of a fixed number of training examples which is less than the
actual dataset and call it a mini-batch. Doing this helps us achieve the advantages of both the former
variants we saw. So, after creating the mini-batches of fixed size, we do the following steps in one
epoch:
1. Pick a mini-batch
2. Feed it to Neural Network
3. Calculate the mean gradient of the mini-batch
4. Use the mean gradient we calculated in step 3 to update the weights
5. Repeat steps 1–4 for the mini-batches we created
Just like SGD, the average cost over the epochs in mini-batch gradient descent fluctuates because we are
averaging a small number of examples at a time. So, when we are using the mini-batch gradient descent
we are updating our parameters frequently as well as we can use vectorized implementation for faster
computations.
Q22. What are the different Deep Learning Frameworks?
• PyTorch: PyTorch is an open source machine learning library based on the Torch library, used for
applications such as computer vision and natural language processing, primarily developed by
Facebook's AI Research lab. It is free and open-source software released under the Modified BSD
license.
• TensorFlow: TensorFlow is a free and open-source software library for dataflow and differentiable
programming across a range of tasks. It is a symbolic math library and is also used for machine learning
applications such as neural networks. Licensed by Apache License 2.0. Developed by Google Brain
Team.
• Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit describes neural networks as a series of
computational steps via a directed graph.
• Keras: Keras is an open-source neural-network library written in Python. It is capable of running on
top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast
experimentation with deep neural networks, it focuses on being user-friendly, modular, and
extensible. Licensed by MIT.
Q23. What is the role of the Activation Function?
The Activation function is used to introduce non-linearity into the neural network helping it to learn more
complex function. Without which the neural network would be only able to learn linear function which is
a linear combination of its input data. An activation function is a function in an artificial neuron that
delivers an output based on inputs.
Q24. Name a few Machine Learning libraries for various purposes.
Purpose Libraries
Scientific Computation Numpy
Tabular Data Pandas, GeoPandas
Data Modelling & Preprocessing Scikit Learn
Time-Series Analysis Statsmodels
Text processing NTLK, Regular Expressions
Deep Learning TensorFlow, Pytorch
Visualization Bokeh, Seaborn
Plotting Matplot
Q25. What is an Auto-Encoder?
https://www.quora.com/What-is-an-autoencoder-What-are-its-applications
Auto-encoders are simple learning networks that aim to transform inputs into outputs with the minimum
possible error. This means that we want the output to be as close to input as possible. We add a couple
of layers between the input and the output, and the sizes of these layers are smaller than the input layer.
The auto-encoder receives unlabeled input which is then encoded to reconstruct the input.
An autoencoder is a type of artificial neural network used to learn efficient data coding in an unsupervised
manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for
dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side,
a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a
representation as close as possible to its original input, hence its name. Several variants exist to the basic
model, with the aim of forcing the learned representations of the input to assume useful properties.
Autoencoders are effectively used for solving many applied problems, from face recognition
to acquiring
the semantic meaning of words.
Q26. What is a Boltzmann Machine?
Boltzmann machines have a simple learning algorithm that allows them to discover interesting features
that represent complex regularities in the training data. The Boltzmann machine is basically used to
optimize the weights and the quantity for the given problem. The learning algorithm is very slow in
networks with many layers of feature detectors. “Restricted Boltzmann Machines” algorithm has a single
layer of feature detectors which makes it faster than the rest.
Q27. What Is Dropout and Batch Normalization?
Dropout is a technique of dropping out hidden and visible nodes of a network randomly to prevent
overfitting of data (typically dropping 20 per cent of the nodes). It doubles the number of iterations
needed to converge the network. It used to avoid overfitting, as it increases the capacity of generalization.
Batch normalization is the technique to improve the performance and stability of neural networks by
normalizing the inputs in every layer so that they have mean output activation of zero and standard
deviation of one.
Q28. Why Is TensorFlow the Most Preferred Library in Deep Learning?
TensorFlow provides both C++ and Python APIs, making it easier to work on and has a faster compilation
time compared to other Deep Learning libraries like Keras and PyTorch. TensorFlow supports both CPU
and GPU computing devices.
Q29. What Do You Mean by Tensor in TensorFlow?
A tensor is a mathematical object represented as arrays of higher dimensions. Think of a n-D matrix. These
arrays of data with different dimensions and ranks fed as input to the neural network are called “Tensors.”
Q30. What is the Computational Graph?
Everything in a TensorFlow is based on creating a computational graph. It has a network of nodes where
each node operates. Nodes represent mathematical operations, and edges represent tensors. Since data
flows in the form of a graph, it is also called a “DataFlow Graph.”
Q31. How is logistic regression done?
Logistic regression measures the relationship between the dependent variable (our label of what we want
to predict) and one or more independent variables (our features) by estimating probability using its
underlying logistic function (sigmoid).
Miscellaneous
Q1. Explain the steps in making a decision tree.
1. Take the entire data set as input
2. Calculate entropy of the target variable, as well as the predictor attributes
3. Calculate your information gain of all attributes (we gain information on sorting different objects from
each other)
4. Choose the attribute with the highest information gain as the root node
5. Repeat the same procedure on every branch until the decision node of each branch is finalized
For example, let's say you want to build a decision tree to decide whether you should accept or decline a
job offer. The decision tree for this case is as shown:
It is clear from the decision tree that an offer is accepted if:
• Salary is greater than $50,000
• The commute is less than an hour
• Coffee is offered
Q2. How do you build a random forest model?
A random forest is built up of a number of decision trees. If you split the data into different packages and
make a decision tree in each of the different groups of data, the random forest brings all those trees
together.
Steps to build a random forest model:
1. Randomly select features from a total of features where <<
2. Among the features, calculate the node D using the best split point
3. Split the node into daughter nodes using the best split
4. Repeat steps two and three until leaf nodes are finalized
5. Build forest by repeating steps one to four for times to create number of trees
Q3. Differentiate between univariate, bivariate, and multivariate analysis.
Univariate
Univariate data contains only one variable. The purpose of the univariate analysis is to describe the data
and find patterns that exist within it.
Example: height of students
Height (in cm)
164
167.3
170
174.2
178
180
The patterns can be studied by drawing conclusions using mean, median, mode, dispersion or range,
minimum, maximum, etc.
Bivariate
Bivariate data involves two different variables. The analysis of this type of data deals with causes and
relationships and the analysis is done to determine the relationship between the two variables.
Example: temperature and ice cream sales in the summer season
Temperature (in Celsius) Sales (in K $)
20 2.0
25 2.1
26 2.3
28 2.7
30 3.1
Here, the relationship is visible from the table that temperature and sales are directly proportional to each
other. The hotter the temperature, the better the sales.
Multivariate
Multivariate data involves three or more variables, it is categorized under multivariate. It is similar to a
bivariate but contains more than one dependent variable.
Example: data for house price prediction
The patterns can be studied by drawing conclusions using mean, median, and mode, dispersion or range,
minimum, maximum, etc. You can start describing the data and using it to guess what the price of the
house will be.
Q4. What are the feature selection methods used to select the right variables?
There are two main methods for feature selection.
Filter Methods
This involves:
• Linear discrimination analysis
• ANOVA
• Chi-Square
The best analogy for selecting features is "bad data in, bad answer out." When we're limiting or selecting
the features, it's all about cleaning up the data coming in.
Wrapper Methods
This involves:
• Forward Selection: We test one feature at a time and keep adding them until we get a good
fit
• Backward Selection: We test all the features and start removing them to see what works
better
• Recursive Feature Elimination: Recursively looks through all the different features and how
they pair together
Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is
performed with the wrapper method.
Q5. In your choice of language, write a program that prints the numbers ranging
from one to 50. But for multiples of three, print "Fizz" instead of the number and
for the multiples of five, print "Buzz." For numbers which are multiples of both three
and five, print "FizzBuzz."
The code is shown below:
for x in range(51):
if x % 3 == 0 and x % 5 == 0:
print(‘fizzbuzz’)
elif x % 3 == 0:
print(‘fizz’)
elif x % 5 == 0:
print(‘buzz’)
else:
print(‘fizzbuzz’)
Q6. You are given a data set consisting of variables with more than 30 percent
missing values. How will you deal with them?
If the data set is large, we can just simply remove the rows with missing data values. It is the quickest way;
we use the rest of the data to predict the values.
For smaller data sets, we can impute missing values with the mean, median, or average of the rest of the
data using pandas data frame in python. There are different ways to do so, such as:
df.mean(), df.fillna(mean)
Other option of imputation is using KNN for numeric or classification values (as KNN just uses k closest
values to impute the missing value).
Q7. For the given points, how will you calculate the Euclidean distance in
Python?
plot1 = [1,3]
plot2 = [2,5]
The Euclidean distance can be calculated as follows:
euclidean_distance = sqrt((plot1[0]-plot2[0])**2 + (plot1[1]-
plot2[1])**2)
Q8. What are dimensionality reduction and its benefits?
Dimensionality reduction refers to the process of converting a data set with vast dimensions into data
with fewer dimensions (fields) to convey similar information concisely.
This reduction helps in compressing data and reducing storage space. It also reduces computation time as
fewer dimensions lead to less computing. It removes redundant features; for example, there's no point in
storing a value in two different units (meters and inches).
Q9. How will you calculate eigenvalues and eigenvectors of the following 3x3
matrix?
Determinant of − 1 and solve to find .
Q10. How should you maintain a deployed model?
The steps to maintain a deployed model are (CREM):
1. Monitor: constant monitoring of all models is needed to determine their performance accuracy.
When you change something, you want to figure out how your changes are going to affect things.
This needs to be monitored to ensure it's doing what it's supposed to do.
2. Evaluate: evaluation metrics of the current model are calculated to determine if a new algorithm
is needed.
3. Compare: the new models are compared to each other to determine which model performs the
best.
4. Rebuild: the best performing model is re-built on the current state of data.
Q11. How can a time-series data be declared as stationery?
Q12. 'People who bought this also bought...' recommendations seen on Amazon
are a result of which algorithm?
The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains
the behavior of other users and their purchase history in terms of ratings, selection, etc.
The engine makes predictions on what might interest a person based on the preferences of other users.
In this algorithm, item features are unknown.
For example, a sales page shows that a certain number of people buy a new phone and also buy tempered
glass at the same time. Next time, when a person buys a phone, he or she may see a recommendation to
buy tempered glass as well.
Q13. What is a Generative Adversarial Network?
Suppose there is a wine shop purchasing wine from dealers, which they resell later. But some dealers sell
fake wine. In this case, the shop owner should be able to distinguish between fake and authentic wine. The
forger will try different techniques to sell fake wine and make sure specific techniques go past the shop
owner’s check. The shop owner would probably get some feedback from wine experts that some of the
wine is not original. The owner would have to improve how he determines whether a wine is fake or
authentic.
The forger’s goal is to create wines that are indistinguishable from the authentic ones while the shop owner
intends to tell if the wine is real or not accurately.
• There is a noise vector coming into the forger who is generating fake wine.
• Here the forger acts as a Generator.
• The shop owner acts as a Discriminator.
• The Discriminator gets two inputs; one is the fake wine, while the other is the real authentic wine.
The shop owner has to figure out whether it is real or fake.
So, there are two primary components of Generative Adversarial Network (GAN) named:
1. Generator
2. Discriminator
The generator is a CNN that keeps keys producing images and is closer in appearance to the real images
while the discriminator tries to determine the difference between real and fake images. The ultimate aim
is to make the discriminator learn to identify real and fake images.
Q14. You are given a dataset on cancer detection. You have built a classification
model and achieved an accuracy of 96 percent. Why shouldn't you be happy with
your model performance? What can you do about it?
Cancer detection results in imbalanced data. In an imbalanced dataset, accuracy should not be based as a
measure of performance. It is important to focus on the remaining four percent, which represents the
patients who were wrongly diagnosed. Early diagnosis is crucial when it comes to cancer detection and
can greatly improve a patient's prognosis.
Hence, to evaluate model performance, we should use Sensitivity (True Positive Rate), Specificity (True
Negative Rate), F measure to determine the class wise performance of the classifier.
Q15. Below are the eight actual values of the target variable in the train file. What
is the entropy of the target variable? [0, 0, 0, 1, 1, 1, 1, 1]
The target variable, in this case, is 1 (the last)
The formula for calculating the entropy is, putting = 5 and = 8, we get:
= −
5
8
5
8
+
3
8
log
3
8
Q16. We want to predict the probability of death from heart disease based on
three risk factors: age, gender, and blood cholesterol level. What is the most
appropriate algorithm for this case? Choose the correct option:
The most appropriate algorithm for this case is logistic regression.
Q17. After studying the behavior of a population, you have identified four specific
individual types that are valuable to your study. You would like to find all users who
are most similar to each individual type. Which algorithm is most appropriate for
this study?
As we are looking for grouping people together specifically by four different similarities, it indicates the
value of k. Therefore, K-means clustering is the most appropriate algorithm for this study.
Q18. You have run the association rules algorithm on your dataset, and the two
rules {banana, apple} => {grape} and {apple, orange} => {grape} have been found to
be relevant. What else must be true? Choose the right answer:
The answer is A: {grape, apple} must be a frequent itemset.
Q19. Your organization has a website where visitors randomly receive one of two
coupons. It is also possible that visitors to the website will not receive a coupon. You
have been asked to determine if offering a coupon to website visitors has any
impact on their purchase decisions. Which analysis method should you use?
One-way ANOVA: in statistics, one-way analysis of variance is a technique that can be used to compare
means of two or more samples. This technique can be used only for numerical response data, the "Y",
usually one variable, and numerical or categorical input data, the "X", always one variable, hence "one-
way".
The ANOVA tests the null hypothesis, which states that samples in all groups are drawn from populations
with the same mean values. To do this, two estimates are made of the population variance. The ANOVA
produces an F-statistic, the ratio of the variance calculated among the means to the variance within the
samples. If the group means are drawn from populations with the same mean values, the variance
between the group means should be lower than the variance of the samples, following the central limit
theorem. A higher ratio therefore implies that the samples were drawn from populations with different
mean values.
Q20. What are the feature vectors?
A feature vector is an n-dimensional vector of numerical features that represent an object. In machine
learning, feature vectors are used to represent numeric or symbolic characteristics (called features) of an
object in a mathematical way that's easy to analyze.
Q21. What is root cause analysis?
Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other
areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor
is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event
from recurring.
Q22. Do gradient descent methods always converge to similar points?
They do not, because in some cases, they reach a local minimum or a local optimum point. You would not
reach the global optimum point. This is governed by the data and the starting conditions.
Q23. What are the most popular Cloud Services used in Data Science?
https://www.zdnet.com/article/the-top-cloud-providers-of-2020-aws-microsoft-azure-google-cloud-
hybrid-saas/
Q24. What is a Canary Deployment?
https://www.split.io/glossary/canary-deployment/
A canary deployment, or canary release, allows you to rollout your features to only a subset of users as
an initial test to make sure nothing else in your system broke.
The initial steps for implementing canary deployment are:
1. create two clones of the production environment,
2. have a load balancer that initially sends all traffic to one version,
3. create new functionality in the other version.
When you deploy the new software version, you shift some percentage – say, 10% – of your user base to
the new version while maintaining 90% of users on the old version. If that 10% reports no errors, you can
roll it out to gradually more users, until the new version is being used by everyone. If the 10% has
problems, though, you can roll it right back, and 90% of your users will have never even seen the problem.
Canary deployment benefits include zero downtime, easy rollout and quick rollback – plus the added
safety from the gradual rollout process. It also has some drawbacks – the expense of maintaining multiple
server instances, the difficult clone-or-don’t-clone database decision.
Typically, software development teams implement blue/green deployment when they’re sure the new
version will work properly and want a simple, fast strategy to deploy it. Conversely, canary deployment is
most useful when the development team isn’t as sure about the new version and they don’t mind a slower
rollout if it means they’ll be able to catch the bugs.
Q25. What is a Blue Green Deployment?
https://docs.cloudfoundry.org/devguide/deploy-apps/blue-green.html
Blue-green deployment is a technique that reduces downtime and risk by running two identical
production environments called Blue and Green.
At any time, only one of the environments is live, with the live environment serving all production traffic.
For this example, Blue is currently live, and Green is idle.
As you prepare a new version of your model, deployment and the final stage of testing takes place in the
environment that is not live: in this example, Green. Once you have deployed and fully tested the model
in Green, you switch the router, so all incoming requests now go to Green instead of Blue. Green is now
live, and Blue is idle.
This technique can eliminate downtime due to app deployment and reduces risk: if something unexpected
happens with your new version on Green, you can immediately roll back to the last version by switching
back to Blue.
This document is based on the original document by Steve Nouri (LinkedIn).
Reviewed and corrected by Davide Callegaro (LinkedIn).
Original credits to kdnuggets, Simplilearn, Edureka, Guru99, Hackernoon,
Datacamp, Nitin Panwar, Michael Rundell.
Below some questions the reader shall view the link of the original article.
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Interview Question Series #2
Python Programming
Numpy
1. Why is python numpy better than lists?
Python numpy arrays should be considered instead of a list because they are fast,
consume less memory and convenient with lots of functionality.
2. Describe the map function in Python?
map function executes the function given as the first argument on all the elements of the
iterable given as the second argument.
3. Generate array of ‘100’ random numbers sampled from a standard normal
distribution using Numpy
np.random.rand(100) will create 100 random numbers generated from standard normal
distribution with mean 0 and standard deviation 1.
4. How to count the occurrence of each value in a numpy array?
Use numpy.bincount()
>>> arr = numpy.array([0, 5, 5, 0, 2, 4, 3, 0, 0, 5, 4, 1, 9, 9])
>>> numpy.bincount(arr)
The argument to bincount() must consist of booleans or positive integers. Negative
integers are invalid.
5. Does Numpy Support Nan?
nan, short for “not a number”, is a special floating point value defined by the IEEE-754
specification. Python numpy supports nan but the definition of nan is more system
dependent and some systems don't have an all round support for it like older cray and vax
computers.
6. What does ravel() function in numpy do?
It combines multiple numpy arrays into a single array
7. What is the meaning of axis=0 and axis=1?
Axis = 0 is meant for reading rows, Axis = 1 is meant for reading columns
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8. What is numpy and describe its use cases?
Numpy is a package library for Python, adding support for large, multi-dimensional arrays
and matrices, along with a large collection of high level mathematical functions. In simple
words, Numpy is an optimized version of Python lists like Financial functions, Linear
Algebra, Statistics, Polynomials, Sorting and Searching etc.
9. How to remove from one array those items that exist in another?
>>> a = np.array([5, 4, 3, 2, 1])
>>> b = np.array([4, 8, 9, 10, 1])
# From 'a' remove all of 'b'
>>> np.setdiff1d(a,b)
# Output:
>>> array([5, 3, 2])
10. How to sort a numpy array by a specific column in a 2D array?
#Choose column 2 as an example
>>> import numpy as np
>>> arr = np.array([[1, 2, 3], [4, 5, 6], [0,0,1]])
>>> arr[arr[:,1].argsort()]
# Output
>>> array([[0, 0, 1], [1, 2, 3], [4, 5, 6]])
11.How to reverse a numpy array in the most efficient way?
>>> import numpy as np
>>> arr = np.array([9, 10, 1, 2, 0])
>>> reverse_arr = arr[::-1]
12.How to calculate percentiles when using numpy?
>>> import numpy as np
>>> arr = np.array([11, 22, 33, 44 ,55 ,66, 77])
>>> perc = np.percentile(arr, 40) #Returns the 40th percentile
>>> print(perc)
13.What Is The Difference Between Numpy And Scipy?
NumPy would contain nothing but the array data type and the most basic operations:
indexing, sorting, reshaping, basic element wise functions, et cetera. All numerical code
would reside in SciPy. SciPy contains more fully-featured versions of the linear algebra
modules, as well as many other numerical algorithms.
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14.What Is The Preferred Way To Check For An Empty (zero Element) Array?
For a numpy array, use the size attribute. The size attribute is helpful for determining the
length of numpy array:
>>> arr = numpy.zeros((1,0))
>>> arr.size
15.What Is The Difference Between Matrices And Arrays?
Matrices can only be two-dimensional, whereas arrays can have any number of
dimensions
16. How can you find the indices of an array where a condition is true?
Given an array a, the condition arr > 3 returns a boolean array and since False is
interpreted as 0 in Python and NumPy.
>>> import numpy as np
>>> arr = np.array([[9,8,7],[6,5,4],[3,2,1]])
>>> arr > 3
>>> array([[True, True, True],
[ True, True, True],
[False, False, False]], dtype=bool)
17. How to find the maximum and minimum value of a given flattened array?
>>> import numpy as np
>>> a = np.arange(4).reshape((2,2))
>>> max_val = np.amax(a)
>>> min_val = np.amin(a)
18. Write a NumPy program to calculate the difference between the maximum and the
minimum values of a given array along the second axis.
>>> import numpy as np
>>> arr = np.arange(16).reshape((4, 7))
>>> res = np.ptp(arr, 1)
19. Find median of a numpy flattened array
>>> import numpy as np
>>> arr = np.arange(16).reshape((4, 5))
>>> res = np.median(arr)
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20. Write a NumPy program to compute the mean, standard deviation, and variance of
a given array along the second axis
import numpy as np
>>> import numpy as np
>>> x = np.arange(16)
>>> mean = np.mean(x)
>>> std = np.std(x)
>>> var= np.var(x)
21. Calculate covariance matrix between two numpy arrays
>>> import numpy as np
>>> x = np.array([2, 1, 0])
>>> y = np.array([2, 3, 3])
>>> cov_arr = np.cov(x, y)
22.Compute Compute pearson product-moment correlation coefficients of two
given numpy arrays
>>> import numpy as np
>>> x = np.array([0, 1, 3])
>>> y = np.array([2, 4, 5])
>>> cross_corr = np.corrcoef(x, y)
23. Develop a numpy program to compute the histogram of nums against the bins
>>> import numpy as np
>>> nums = np.array([0.5, 0.7, 1.0, 1.2, 1.3, 2.1])
>>> bins = np.array([0, 1, 2, 3])
>>> np.histogram(nums, bins)
24. Get the powers of an array values element-wise
>>> import numpy as np
>>> x = np.arange(7)
>>> np.power(x, 3)
25. Write a NumPy program to get true division of the element-wise array inputs
>>> import numpy as np
>>> x = np.arange(10)
>>> np.true_divide(x, 3)
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Pandas
26.What is a series in pandas?
A Series is defined as a one-dimensional array that is capable of storing various data
types. The row labels of the series are called the index. By using a 'series' method, we
can easily convert the list, tuple, and dictionary into series. A Series cannot contain
multiple columns.
27.What features make Pandas such a reliable option to store tabular data?
Memory Efficient, Data Alignment, Reshaping, Merge and join and Time Series.
28.What is reindexing in pandas?
Reindexing is used to conform DataFrame to a new index with optional filling logic. It
places NA/NaN in that location where the values are not present in the previous index. It
returns a new object unless the new index is produced as equivalent to the current one,
and the value of copy becomes False. It is used to change the index of the rows and
columns of the DataFrame.
29.How will you create a series from dict in Pandas?
A Series is defined as a one-dimensional array that is capable of storing various data
types.
>>> import pandas as pd
>>> info = {'x' : 0., 'y' : 1., 'z' : 2.}
>>> a = pd.Series(info)
30.How can we create a copy of the series in Pandas?
Use pandas.Series.copy method
>>> import pandas as pd
>>> pd.Series.copy(deep=True)
31.What is groupby in Pandas?
GroupBy is used to split the data into groups. It groups the data based on some criteria.
Grouping also provides a mapping of labels to the group names. It has a lot of variations
that can be defined with the parameters and makes the task of splitting the data quick and
easy.
32.What is vectorization in Pandas?
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Vectorization is the process of running operations on the entire array. This is done to
reduce the amount of iteration performed by the functions. Pandas have a number of
vectorized functions like aggregations, and string functions that are optimized to operate
specifically on series and DataFrames. So it is preferred to use the vectorized pandas
functions to execute the operations quickly.
33.Mention the different types of Data Structures in Pandas
Pandas provide two data structures, which are supported by the pandas library, Series,
and DataFrames. Both of these data structures are built on top of the NumPy.
34.What Is Time Series In pandas
A time series is an ordered sequence of data which basically represents how some
quantity changes over time. pandas contains extensive capabilities and features for
working with time series data for all domains.
35.How to convert pandas dataframe to numpy array?
The function to_numpy() is used to convert the DataFrame to a NumPy array.
DataFrame.to_numpy(self, dtype=None, copy=False)
The dtype parameter defines the data type to pass to the array and the copy ensures the
returned value is not a view on another array.
36.Write a Pandas program to get the first 5 rows of a given DataFrame
>>> import pandas as pd
>>> exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael',
'Matthew', 'Laura', 'Kevin', 'Jonas'],}
labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
>>> df = pd.DataFrame(exam_data , index=labels)
>>> df.iloc[:5]
37.Develop a Pandas program to create and display a one-dimensional array-
like object containing an array of data.
>>> import pandas as pd
>>> pd.Series([2, 4, 6, 8, 10])
38.Write a Python program to convert a Panda module Series to Python list and
it's type.
>>> import pandas as pd
>>> ds = pd.Series([2, 4, 6, 8, 10])
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>>> type(ds)
>>> ds.tolist()
>>> type(ds.tolist())
39.Develop a Pandas program to add, subtract, multiple and divide two Pandas
Series.
>>> import pandas as pd
>>> ds1 = pd.Series([2, 4, 6, 8, 10])
>>> ds2 = pd.Series([1, 3, 5, 7, 9])
>>> sum = ds1 + ds2
>>> sub = ds1 - ds2
>>> mul = ds1 * ds2
>>> div = ds1 / ds2
40.Develop a Pandas program to compare the elements of the two Pandas
Series.
>>> import pandas as pd
>>> ds1 = pd.Series([2, 4, 6, 8, 10])
>>> ds2 = pd.Series([1, 3, 5, 7, 10])
>>> ds1 == ds2
>>> ds1 > ds2
>>> ds1 < ds2
41.Develop a Pandas program to change the data type of given a column or a
Series.
>>> import pandas as pd
>>> s1 = pd.Series(['100', '200', 'python', '300.12', '400'])
>>> s2 = pd.to_numeric(s1, errors='coerce')
>>> s2
42.Write a Pandas program to convert Series of lists to one Series
>>> import pandas as pd
>>> s = pd.Series([ ['Red', 'Black'], ['Red', 'Green', 'White'] , ['Yellow']])
>>> s = s.apply(pd.Series).stack().reset_index(drop=True)
43.Write a Pandas program to create a subset of a given series based on value
and condition
>>> import pandas as pd
>>> s = pd.Series([0, 1,2,3,4,5,6,7,8,9,10])
>>> n = 6
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>>> new_s = s[s < n]
>>> new_s
44.Develop a Pandas code to alter the order of index in a given series
>>> import pandas as pd
>>> s = pd.Series(data = [1,2,3,4,5], index = ['A', 'B', 'C','D','E'])
>>> s.reindex(index = ['B','A','C','D','E'])
45.Write a Pandas code to get the items of a given series not present in another
given series.
>>> import pandas as pd
>>> sr1 = pd.Series([1, 2, 3, 4, 5])
>>> sr2 = pd.Series([2, 4, 6, 8, 10])
>>> result = sr1[~sr1.isin(sr2)]
>>> result
46.What is the difference between the two data series df[‘Name’] and df.loc[:,
‘Name’]?
>>> First one is a view of the original dataframe and second one is a copy of the original
dataframe.
47.Write a Pandas program to display the most frequent value in a given series
and replace everything else as “replaced” in the series.
>>> import pandas as pd
>>> import numpy as np
>>> np.random.RandomState(100)
>>> num_series = pd.Series(np.random.randint(1, 5, [15]))
>>> result = num_series[~num_series.isin(num_series.value_counts().index[:1])] =
'replaced'
48.Write a Pandas program to find the positions of numbers that are multiples
of 5 of a given series.
>>> import pandas as pd
>>> import numpy as np
>>> num_series = pd.Series(np.random.randint(1, 10, 9))
>>> result = np.argwhere(num_series % 5==0)
49.How will you add a column to a pandas DataFrame?
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# importing the pandas library
>>> import pandas as pd
>>> info = {'one' : pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e']),
'two' : pd.Series([1, 2, 3, 4, 5, 6], index=['a', 'b', 'c', 'd', 'e', 'f'])}
>>> info = pd.DataFrame(info)
# Add a new column to an existing DataFrame object
>>> info['three']=pd.Series([20,40,60],index=['a','b','c'])
50.How to iterate over a Pandas DataFrame?
You can iterate over the rows of the DataFrame by using for loop in combination with an
iterrows() call on the DataFrame.
Python Language
51.What type of language is python? Programming or scripting?
Python is capable of scripting, but in general sense, it is considered as a general-purpose
programming language.
52.Is python case sensitive?
Yes, python is a case sensitive language.
53.What is a lambda function in python?
An anonymous function is known as a lambda function. This function can have any
number of parameters but can have just one statement.
54.What is the difference between xrange and xrange in python?
xrange and range are the exact same in terms of functionality.The only difference is that
range returns a Python list object and x range returns an xrange object.
55.What are docstrings in python?
Docstrings are not actually comments, but they are documentation strings. These
docstrings are within triple quotes. They are not assigned to any variable and therefore,
at times, serve the purpose of comments as well.
56.Whenever Python exits, why isn’t all the memory deallocated?
Whenever Python exits, especially those Python modules which are having circular
references to other objects or the objects that are referenced from the global namespaces
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are not always de-allocated or freed. It is impossible to de-allocate those portions of
memory that are reserved by the C library. On exit, because of having its own efficient
clean up mechanism, Python would try to de-allocate/destroy every other object.
57.What does this mean: *args, **kwargs? And why would we use it?
We use *args when we aren’t sure how many arguments are going to be passed to a
function, or if we want to pass a stored list or tuple of arguments to a function. **kwargs is
used when we don’t know how many keyword arguments will be passed to a function, or
it can be used to pass the values of a dictionary as keyword arguments.
58.What is the difference between deep and shallow copy?
Shallow copy is used when a new instance type gets created and it keeps the values that
are copied in the new instance. Shallow copy is used to copy the reference pointers just
like it copies the values.
Deep copy is used to store the values that are already copied. Deep copy doesn’t copy
the reference pointers to the objects. It makes the reference to an object and the new
object that is pointed by some other object gets stored.
59.Define encapsulation in Python?
Encapsulation means binding the code and the data together. A Python class in an
example of encapsulation.
60.Does python make use of access specifiers?
Python does not deprive access to an instance variable or function. Python lays down the
concept of prefixing the name of the variable, function or method with a single or double
underscore to imitate the behavior of protected and private access specifiers.
61.What are the generators in Python?
Generators are a way of implementing iterators. A generator function is a normal function
except that it contains yield expression in the function definition making it a generator
function.
62.How will you remove the duplicate elements from the given list?
The set is another type available in Python. It doesn’t allow copies and provides some
good functions to perform set operations like union, difference etc.
>>> list(set(a))
63.Does Python allow arguments Pass by Value or Pass by Reference?
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Neither the arguments are Pass by Value nor does Python supports Pass by reference.
Instead, they are Pass by assignment. The parameter which you pass is originally a
reference to the object not the reference to a fixed memory location. But the reference is
passed by value. Additionally, some data types like strings and tuples are immutable
whereas others are mutable.
64.What is slicing in Python?
Slicing in Python is a mechanism to select a range of items from Sequence types like
strings, list, tuple, etc.
65.Why is the “pass” keyword used in Python?
The “pass” keyword is a no-operation statement in Python. It signals that no action is
required. It works as a placeholder in compound statements which are intentionally left
blank.
66.What is PEP8 and why is it important?
PEP stands for Python Enhancement Proposal. A PEP is an official design document
providing information to the Python Community, or describing a new feature for Python or
its processes. PEP 8 is especially important since it documents the style guidelines for
Python Code. Apparently contributing in the Python open-source community requires you
to follow these style guidelines sincerely and strictly.
67.What are decorators in Python?
Decorators in Python are essentially functions that add functionality to an existing function
in Python without changing the structure of the function itself. They are represented by the
@decorator_name in Python and are called in bottom-up fashion
68.What is the key difference between lists and tuples in python?
The key difference between the two is that while lists are mutable, tuples on the other
hand are immutable objects.
69.What is self in Python?
Self is a keyword in Python used to define an instance or an object of a class. In Python,
it is explicitly used as the first parameter, unlike in Java where it is optional. It helps in
distinguishing between the methods and attributes of a class from its local variables.
70.What is PYTHONPATH in Python?
PYTHONPATH is an environment variable which you can set to add additional directories
where Python will look for modules and packages. This is especially useful in maintaining
Python libraries that you do not wish to install in the global default location.
71.What is the difference between .py and .pyc files?
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.py files contain the source code of a program. Whereas, .pyc file contains the bytecode
of your program. We get bytecode after compilation of .py file (source code). .pyc files are
not created for all the files that you run. It is only created for the files that you import.
72.Explain how you can access a module written in Python from C?
You can access a module written in Python from C by following method,
Module = =PyImport_ImportModule("<modulename>");
73.What is namespace in Python?
In Python, every name introduced has a place where it lives and can be hooked for. This
is known as namespace. It is like a box where a variable name is mapped to the object
placed. Whenever the variable is searched out, this box will be searched, to get the
corresponding object.
74.What is pickling and unpickling?
Pickle module accepts any Python object and converts it into a string representation and
dumps it into a file by using the dump function, this process is called pickling. While the
process of retrieving original Python objects from the stored string representation is called
unpickling.
75.How is Python interpreted?
Python language is an interpreted language. The Python program runs directly from the
source code. It converts the source code that is written by the programmer into an
intermediate language, which is again translated into machine language that has to be
executed.
Jupyter Notebook
76.What is the main use of a Jupyter notebook?
Jupyter Notebook is an open-source web application that allows us to create and share
codes and documents. It provides an environment, where you can document your code,
run it, look at the outcome, visualize data and see the results without leaving the
environment.
77.How do I increase the cell width of the Jupyter/ipython notebook in my
browser?
>>> from IPython.core.display import display, HTML
>>> display(HTML("<style>.container { width:100% !important; }</style>"))
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78.How do I convert an IPython Notebook into a Python file via command line?
>>> jupyter nbconvert --to script [YOUR_NOTEBOOK].ipynb
79.How to measure execution time in a jupyter notebook?
>>> %%time is inbuilt magic command
80.How to run a jupyter notebook from the command line?
>>> jupyter nbconvert --to python nb.ipynb
81.How to make inline plots larger in jupyter notebooks?
Use figure size.
>>> fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')
82.How to display multiple images in a jupyter notebook?
>>>for ima in images:
>>>plt.figure()
>>>plt.imshow(ima)
83.Why is the Jupyter notebook interactive code and data exploration friendly?
The ipywidgets package provides many common user interface controls for exploring code
and data interactively.
84.What is the default formatting option in jupyter notebook?
Default formatting option is markdown
85.What are kernel wrappers in jupyter?
Jupyter brings a lightweight interface for kernel languages that can be wrapped in Python.
Wrapper kernels can implement optional methods, notably for code completion and code
inspection.
86.What are the advantages of custom magic commands?
Create IPython extensions with custom magic commands to make interactive computing
even easier. Many third-party extensions and magic commands exist, for example, the
%%cython magic that allows one to write Cython code directly in a notebook.
87.Is the jupyter architecture language dependent?
No. It is language independent.
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88.Which tools allow jupyter notebooks to easily convert to pdf and html?
Nbconvert converts it to pdf and html while Nbviewer renders the notebooks on the web
platforms.
89.What is a major disadvantage of a Jupyter notebook?
It is very hard to run long asynchronous tasks. Less Secure.
90.In which domain is the jupyter notebook widely used?
It is mainly used for data analysis and machine learning related tasks.
91.What are alternatives to jupyter notebook?
PyCharm interact, VS Code Python Interactive etc.
92.Where can you make configuration changes to the jupyter notebook?
In the config file located at ~/.ipython/profile_default/ipython_config.py
93.Which magic command is used to run python code from jupyter notebook?
%run can execute python code from .py files
94.How to pass variables across the notebooks?
The %store command lets you pass variables between two different notebooks.
>>> data = 'this is the string I want to pass to different notebook'
>>> %store data
# Stored 'data' (str)
# In new notebook
>>> %store -r data
>>> print(data)
95.Export the contents of a cell/Show the contents of an external script
Using the %%writefile magic saves the contents of that cell to an external file. %pycat
does the opposite and shows you (in a popup) the syntax highlighted contents of an
external file.
96.What inbuilt tool we use for debugging python code in a jupyter notebook?
Jupyter has its own interface for The Python Debugger (pdb). This makes it possible to go
inside the function and investigate what happens there.
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97.How to make high resolution plots in a jupyter notebook?
>>> %config InlineBackend.figure_format ='retina'
98.How can one use latex in a jupyter notebook?
When you write LaTeX in a Markdown cell, it will be rendered as a formula using MathJax.
99. What is a jupyter lab?
It is a next generation user interface for conventional jupyter notebooks. Users can drag
and drop cells, arrange code workspace and live previews. It’s still in the early stage of
development.
100. What is the biggest limitation for a Jupyter notebook?
Code versioning, management and debugging is not scalable in current jupyter notebook.
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References
[1] https://www.edureka.co
[2] https://www.kausalvikash.in
[3] https://www.wisdomjobs.com
[4] https://blog.edugrad.com
[5]https://stackoverflow.com
[6]http://www.ezdev.org
[7]https://www.techbeamers.com
[8]https://www.w3resource.com
[9]https://www.javatpoint.com
[10]https://analyticsindiamag.com
[11]https://www.onlineinterviewquestions.com
[12]https://www.geeksforgeeks.org
[13]https://www.springpeople.com
[14]https://atraininghub.com
[15]https://www.interviewcake.com
[16]https://www.techbeamers.com
[17]https://www.tutorialspoint.com
[18]https://programmingwithmosh.com
[19]https://www.interviewbit.com
[20]https://www.guru99.com
[21]https://hub.packtpub.com
[22]https://analyticsindiamag.com
[23]https://www.dataquest.io
[24]https://www.infoworld.com
Top 100 Machine Learning Questions & Answers
Steve Nouri
Q1 Explain the difference between supervised and unsupervised machine
learning?
In supervised machine learning algorithms, we have to provide labeled data, for example,
prediction of stock market prices, whereas in unsupervised we need not have labeled data, for
example, classification of emails into spam and non-spam.
Q2 What are the parametric models? Give an example.
Parametric models are those with a finite number of parameters. To predict new data, you only
need to know the parameters of the model. Examples include linear regression, logistic
regression, and linear SVMs.
Non-parametric models are those with an unbounded number of parameters, allowing for more
flexibility. To predict new data, you need to know the parameters of the model and the state of
the data that has been observed. Examples include decision trees, k-nearest neighbors, and
topic models using latent Dirichlet analysis.
Q3 What is the difference between classification and regression?
Classification is used to produce discrete results, classification is used to classify data into some
specific categories. For example, classifying emails into spam and non-spam categories.
Whereas, We use regression analysis when we are dealing with continuous data, for example
predicting stock prices at a certain point in time.
Q4 What Is Overfitting, and How Can You Avoid It?
Overfitting is a situation that occurs when a model learns the training set too well, taking up
random fluctuations in the training data as concepts. These impact the model’s ability to
generalize and don’t apply to new data.
When a model is given the training data, it shows 100 percent accuracy—technically a slight
loss. But, when we use the test data, there may be an error and low efficiency. This condition is
known as overfitting.
There are multiple ways of avoiding overfitting, such as:
● Regularization. It involves a cost term for the features involved with the objective function
● Making a simple model. With lesser variables and parameters, the variance can be
reduced
● Cross-validation methods like k-folds can also be used
● If some model parameters are likely to cause overfitting, techniques for regularization
like LASSO can be used that penalize these parameters
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Q5 What is meant by ‘Training set’ and ‘Test Set’?
We split the given data set into two different sections namely,’Training set’ and ‘Test Set’.
‘Training set’ is the portion of the dataset used to train the model.
‘Testing set’ is the portion of the dataset used to test the trained model.
Q6 How Do You Handle Missing or Corrupted Data in a Dataset?
One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or
replace them entirely with some other value.
There are two useful methods in Pandas:
● IsNull() and dropna() will help to find the columns/rows with missing data and drop them
● Fillna() will replace the wrong values with a placeholder value
Q7 Explain Ensemble learning.
In ensemble learning, many base models like classifiers and regressors are generated and
combined together so that they give better results. It is used when we build component
classifiers that are accurate and independent. There are sequential as well as parallel ensemble
methods.
Q8 Explain the Bias-Variance Tradeoff.
Predictive models have a tradeoff between bias (how well the model fits the data) and variance
(how much the model changes based on changes in the inputs).
Simpler models are stable (low variance) but they don't get close to the truth (high bias).
More complex models are more prone to overfitting (high variance) but they are expressive
enough to get close to the truth (low bias). The best model for a given problem usually lies
somewhere in the middle.
Q9 What is the difference between stochastic gradient descent (SGD) and
gradient descent (GD)?
Both algorithms are methods for finding a set of parameters that minimize a loss function by
evaluating parameters against data and then making adjustments.
In standard gradient descent, you'll evaluate all training samples for each set of parameters.
This is akin to taking big, slow steps toward the solution.
In stochastic gradient descent, you'll evaluate only 1 training sample for the set of parameters
before updating them. This is akin to taking small, quick steps toward the solution.
Q10 How Can You Choose a Classifier Based on a Training Set Data Size?
When the training set is small, a model that has a right bias and low variance seems to work
better because they are less likely to overfit.
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For example, Naive Bayes works best when the training set is large. Models with low bias and
high variance tend to perform better as they work fine with complex relationships.
Q11 What are 3 data preprocessing techniques to handle outliers?
1. Winsorize (cap at threshold).
2. Transform to reduce skew (using Box-Cox or similar).
3. Remove outliers if you're certain they are anomalies or measurement errors.
Q12 How much data should you allocate for your training, validation, and test
sets?
You have to find a balance, and there's no right answer for every problem.
If your test set is too small, you'll have an unreliable estimation of model performance
(performance statistic will have high variance). If your training set is too small, your actual model
parameters will have a high variance.
A good rule of thumb is to use an 80/20 train/test split. Then, your train set can be further split
into train/validation or into partitions for cross-validation.
Q13 What Is a False Positive and False Negative and How Are They Significant?
False positives are those cases which wrongly get classified as True but are False.
False negatives are those cases which wrongly get classified as False but are True.
In the term ‘False Positive,’ the word ‘Positive’ refers to the ‘Yes’ row of the predicted value in
the confusion matrix. The complete term indicates that the system has predicted it as a positive,
but the actual value is negative.
Q14 Explain the difference between L1 and L2 regularization.
L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with
many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a
Laplacean prior to the terms, while L2 corresponds to a Gaussian prior.
Q15 What’s a Fourier transform?
A Fourier transform is a generic method to decompose generic functions into a superposition of
symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find
the recipe. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to
match any time signal. A Fourier transform converts a signal from time to frequency domain —
it’s a very common way to extract features from audio signals or other time series such as
sensor data.
Q16 What is deep learning, and how does it contrast with other machine learning
algorithms?
Deep learning is a subset of machine learning that is concerned with neural networks: how to
use backpropagation and certain principles from neuroscience to more accurately model large
sets of unlabelled or semi-structured data. In that sense, deep learning represents an
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unsupervised learning algorithm that learns representations of data through the use of neural
nets.
Q17 What’s the difference between a generative and discriminative model?
A generative model will learn categories of data while a discriminative model will simply learn
the distinction between different categories of data. Discriminative models will generally
outperform generative models on classification tasks.
Q18 What Are the Applications of Supervised Machine Learning in Modern
Businesses?
Applications of supervised machine learning include:
● Email Spam Detection
Here we train the model using historical data that consists of emails categorized as
spam or not spam. This labeled information is fed as input to the model.
● Healthcare Diagnosis
By providing images regarding a disease, a model can be trained to detect if a person is
suffering from the disease or not.
● Sentiment Analysis
This refers to the process of using algorithms to mine documents and determine
whether they’re positive, neutral, or negative in sentiment.
● Fraud Detection
Training the model to identify suspicious patterns, we can detect instances of possible
fraud.
Q19 What Is Semi-supervised Machine Learning?
Supervised learning uses data that is completely labeled, whereas unsupervised learning uses
no training data.
In the case of semi-supervised learning, the training data contains a small amount of labeled
data and a large amount of unlabeled data.
Q20. What Are Unsupervised Machine Learning Techniques?
There are two techniques used in unsupervised learning: clustering and association.
Clustering
● Clustering problems involve data to be divided into subsets. These subsets, also called
clusters, contain data that are similar to each other. Different clusters reveal different
details about the objects, unlike classification or regression.
Association
● In an association problem, we identify patterns of associations between different
variables or items.
● For example, an eCommerce website can suggest other items for you to buy, based on
the prior purchases that you have made, spending habits, items in your wishlist, other
customers’ purchase habits, and so on.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Q21 What Is ‘naive’ in the Naive Bayes Classifier?
The classifier is called ‘naive’ because it makes assumptions that may or may not turn out to be
correct.
The algorithm assumes that the presence of one feature of a class is not related to the presence
of any other feature (absolute independence of features), given the class variable.
For instance, a fruit may be considered to be a cherry if it is red in color and round in shape,
regardless of other features. This assumption may or may not be right (as an apple also
matches the description).
Q22 Explain Latent Dirichlet Allocation (LDA).
Latent Dirichlet Allocation (LDA) is a common method of topic modeling, or classifying
documents by subject matter.
LDA is a generative model that represents documents as a mixture of topics that each have
their own probability distribution of possible words.
The "Dirichlet" distribution is simply a distribution of distributions. In LDA, documents are
distributions of topics that are distributions of words.
Q23 Explain Principle Component Analysis (PCA).
PCA is a method for transforming features in a dataset by combining them into uncorrelated
linear combinations.
These new features, or principal components, sequentially maximize the variance represented
(i.e. the first principal component has the most variance, the second principal component has
the second most, and so on).
As a result, PCA is useful for dimensionality reduction because you can set an arbitrary
variance cutoff.
Q24 What’s the F1 score? How would you use it?
The F1 score is a measure of a model’s performance. It is a weighted average of the precision
and recall of a model, with results tending to 1 being the best, and those tending to 0 being the
worst. You would use it in classification tests where true negatives don’t matter much.
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Q25 When should you use classification over regression?
Classification produces discrete values and dataset to strict categories, while regression gives
you continuous results that allow you to better distinguish differences between individual points.
You would use classification over regression if you wanted your results to reflect the
belongingness of data points in your dataset to certain explicit categories (ex: If you wanted to
know whether a name was male or female rather than just how correlated they were with male
and female names.)
Q26 How do you ensure you’re not overfitting with a model?
This is a simple restatement of a fundamental problem in machine learning: the possibility of
overfitting training data and carrying the noise of that data through to the test set, thereby
providing inaccurate generalizations.
There are three main methods to avoid overfitting:
1- Keep the model simpler: reduce variance by taking into account fewer variables and
parameters, thereby removing some of the noise in the training data.
2- Use cross-validation techniques such as k-folds cross-validation.
3- Use regularization techniques such as LASSO that penalize certain model parameters if
they’re likely to cause overfitting.
Q27 How Will You Know Which Machine Learning Algorithm to Choose for Your
Classification Problem?
While there is no fixed rule to choose an algorithm for a classification problem, you can follow
these guidelines:
● If accuracy is a concern, test different algorithms and cross-validate them
● If the training dataset is small, use models that have low variance and high bias
● If the training dataset is large, use models that have high variance and little bias
Q28 How Do You Design an Email Spam Filter?
Building a spam filter involves the following process:
● The email spam filter will be fed with thousands of emails
● Each of these emails already has a label: ‘spam’ or ‘not spam.’
● The supervised machine learning algorithm will then determine which type of emails are
being marked as spam based on spam words like the lottery, free offer, no money, full
refund, etc.
● The next time an email is about to hit your inbox, the spam filter will use statistical
analysis and algorithms like Decision Trees and SVM to determine how likely the email
is spam
● If the likelihood is high, it will label it as spam, and the email won’t hit your inbox
Steve Nouri https://www.linkedin.com/in/stevenouri/
● Based on the accuracy of each model, we will use the algorithm with the highest
accuracy after testing all the models
Q29 What evaluation approaches would you work to gauge the effectiveness of a
machine learning model?
You would first split the dataset into training and test sets, or perhaps use cross-validation
techniques to further segment the dataset into composite sets of training and test sets within the
data. You should then implement a choice selection of performance metrics: here is a fairly
comprehensive list. You could use measures such as the F1 score, the accuracy, and the
confusion matrix. What’s important here is to demonstrate that you understand the nuances of
how a model is measured and how to choose the right performance measures for the right
situations.
Q30 How would you implement a recommendation system for our company’s
users?
A lot of machine learning interview questions of this type will involve the implementation of
machine learning models to a company’s problems. You’ll have to research the company and its
industry in-depth, especially the revenue drivers the company has, and the types of users the
company takes on in the context of the industry it’s in.
Q31 Explain bagging.
Bagging, or Bootstrap Aggregating, is an ensemble method in which the dataset is first divided
into multiple subsets through resampling.
Then, each subset is used to train a model, and the final predictions are made through voting or
averaging the component models.
Bagging is performed in parallel.
Q32 What is the ROC Curve and what is AUC (a.k.a. AUROC)?
The ROC (receiver operating characteristic) the performance plot for binary classifiers of True
Positive Rate (y-axis) vs. False Positive Rate (x-
axis).
AUC is the area under the ROC curve, and it's a common performance metric for evaluating
binary classification models.
It's equivalent to the expected probability that a uniformly drawn random positive is ranked
before a uniformly drawn random negative.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Q33 Why is Area Under ROC Curve (AUROC) better than raw accuracy as an
out-of-sample evaluation metric?
AUROC is robust to class imbalance, unlike raw accuracy.
For example, if you want to detect a type of cancer that's prevalent in only 1% of the population,
you can build a model that achieves 99% accuracy by simply classifying everyone has
cancer-free.
Q34 What are the advantages and disadvantages of neural networks?
Advantages: Neural networks (specifically deep NNs) have led to performance breakthroughs
for unstructured datasets such as images, audio, and video. Their incredible flexibility allows
them to learn patterns that no other ML algorithm can learn.
Disadvantages: However, they require a large amount of training data to converge. It's also
difficult to pick the right architecture, and the internal "hidden" layers are incomprehensible.
Q35 Define Precision and Recall.
Precision
● Precision is the ratio of several events you can correctly recall to the total number of
events you recall (mix of correct and wrong recalls).
● Precision = (True Positive) / (True Positive + False Positive)
Recall
● A recall is the ratio of a number of events you can recall the number of total events.
● Recall = (True Positive) / (True Positive + False Negative)
Q36 What Is Decision Tree Classification?
A decision tree builds classification (or regression) models as a tree structure, with datasets
broken up into ever-smaller subsets while developing the decision tree, literally in a tree-like
way with branches and nodes. Decision trees can handle both categorical and numerical data.
Q37 What Is Pruning in Decision Trees, and How Is It Done?
Pruning is a technique in machine learning that reduces the size of decision trees. It reduces the
complexity of the final classifier, and hence improves predictive accuracy by the reduction of
overfitting.
Pruning can occur in:
● Top-down fashion. It will traverse nodes and trim subtrees starting at the root
● Bottom-up fashion. It will begin at the leaf nodes
There is a popular pruning algorithm called reduced error pruning, in which:
● Starting at the leaves, each node is replaced with its most popular class
● If the prediction accuracy is not affected, the change is kept
● There is an advantage of simplicity and speed
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Q38 What Is a Recommendation System?
Anyone who has used Spotify or shopped at Amazon will recognize a recommendation system:
It’s an information filtering system that predicts what a user might want to hear or see based on
choice patterns provided by the user.
Q39 What Is Kernel SVM?
Kernel SVM is the abbreviated version of the kernel support vector machine. Kernel methods
are a class of algorithms for pattern analysis, and the most common one is the kernel SVM.
Q40 What Are Some Methods of Reducing Dimensionality?
You can reduce dimensionality by combining features with feature engineering, removing
collinear features, or using algorithmic dimensionality reduction.
Now that you have gone through these machine learning interview questions, you must have
got an idea of your strengths and weaknesses in this domain.
Q41 What Are the Three Stages of Building a Model in Machine Learning?
The three stages of building a machine learning model are:
● Model Building Choose a suitable algorithm for the model and train it according to the
requirement
● Model Testing Check the accuracy of the model through the test data
● Applying the Mode Make the required changes after testing and use the final model for
real-time projects. Here, it’s important to remember that once in a while, the model
needs to be checked to make sure it’s working correctly. It should be modified to make
sure that it is up-to-date.
Q42 How is KNN different from k-means clustering?
K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an
unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this
really means is that in order for K-Nearest Neighbors to work, you need labeled data you want
to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering requires
only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and
gradually learn how to cluster them into groups by computing the mean of the distance between
different points.
Q43 Mention the difference between Data Mining and Machine learning?
Machine learning relates to the study, design, and development of the algorithms that give
computers the capability to learn without being explicitly programmed. While data mining can
be defined as the process in which the unstructured data tries to extract knowledge or unknown
interesting patterns. During this processing machine, learning algorithms are used.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Q44 What are the different Algorithm techniques in Machine Learning?
The different types of techniques in Machine Learning are
● Supervised Learning
● Unsupervised Learning
● Semi-supervised Learning
● Reinforcement Learning
● Transduction
● Learning to Learn
Q45 You are given a data set. The data set has missing values that spread along 1
standard deviation from the median. What percentage of data would remain
unaffected? Why?
This question has enough hints for you to start thinking! Since the data is spread across the
median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the
data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data
unaffected. Therefore, ~32% of the data would remain unaffected by missing values.
Q46 What are PCA, KPCA, and ICA used for?
PCA (Principal Components Analysis), KPCA ( Kernel-based Principal Component Analysis)
and ICA ( Independent Component Analysis) are important feature extraction techniques used
for dimensionality reduction.
Q47 What are support vector machines?
Support vector machines are supervised learning algorithms used for classification and
regression analysis.
Q48 What is batch statistical learning?
Statistical learning techniques allow learning a function or predictor from a set of observed data
that can make predictions about unseen or future data. These techniques provide guarantees
on the performance of the learned predictor on the future unseen data based on a statistical
assumption on the data generating process.
Q49 What is the bias-variance decomposition of classification error in the
ensemble method?
The expected error of a learning algorithm can be decomposed into bias and variance. A bias
term measures how closely the average classifier produced by the learning algorithm matches
the target function. The variance term measures how much the learning algorithm’s prediction
fluctuates for different training sets.
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Q50 When is Ridge regression favorable over Lasso regression?
You can quote ISLR’s authors Hastie, Tibshirani who asserted that, in the presence of few
variables with medium / large sized effect, use lasso regression. In presence of many variables
with small/medium-sized effects, use ridge regression.
Conceptually, we can say, lasso regression (L1) does both variable selection and parameter
shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all
the coefficients in the model. In the presence of correlated variables, ridge regression might be
the preferred choice. Also, ridge regression works best in situations where the least square
estimates have higher variance. Therefore, it depends on our model objective.
Q51 You’ve built a random forest model with 10000 trees. You got delighted after
getting training error as 0.00. But, the validation error is 34.23. What is going on?
Haven’t you trained your model perfectly?
The model has overfitted. Training error 0.00 means the classifier has mimicked the training
data patterns to an extent, that they are not available in the unseen data. Hence, when this
classifier was run on an unseen sample, it couldn’t find those patterns and returned predictions
with higher error. In a random forest, it happens when we use a larger number of trees than
necessary. Hence, to avoid this situation, we should tune the number of trees using
cross-validation.
Q50 What is a convex hull?
In the case of linearly separable data, the convex hull represents the outer boundaries of the
two groups of data points. Once the convex hull is created, we get maximum margin hyperplane
(MMH) as a perpendicular bisector between two convex hulls. MMH is the line which attempts to
create the greatest separation between two groups.
Q51 What do you understand by Type I vs Type II error?
Type I error is committed when the null hypothesis is true and we reject it, also known as a
‘False Positive’. Type II error is committed when the null hypothesis is false and we accept it,
also known as ‘False Negative’.
In the context of the confusion matrix, we can say Type I error occurs when we classify a value
as positive (1) when it is actually negative (0). Type II error occurs when we classify a value as
negative (0) when it is actually positive(1).
Q52. In k-means or kNN, we use euclidean distance to calculate the distance
between nearest neighbors. Why not manhattan distance?
We don’t use manhattan distance because it calculates distance horizontally or vertically only. It
has dimension restrictions. On the other hand, the euclidean metric can be used in any space to
calculate distance. Since the data points can be present in any dimension, euclidean distance is
a more viable option.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Example: Think of a chessboard, the movement made by a bishop or a rook is calculated by
manhattan distance because of their respective vertical & horizontal movements.
Q53 Do you suggest that treating a categorical variable as a continuous variable
would result in a better predictive model?
For better predictions, the categorical variable can be considered as a continuous variable only
when the variable is ordinal in nature.
Q54 OLS is to linear regression. The maximum likelihood is logistic regression.
Explain the statement.
OLS and Maximum likelihood are the methods used by the respective regression methods to
approximate the unknown parameter (coefficient) value. In simple words,
Ordinary least square(OLS) is a method used in linear regression which approximates the
parameters resulting in minimum distance between actual and predicted values. Maximum
Likelihood helps in choosing the values of parameters which maximizes the likelihood that the
parameters are most likely to produce observed data.
Q55 When does regularization becomes necessary in Machine Learning?
Regularization becomes necessary when the model begins to overfit/underfit. This technique
introduces a cost term for bringing in more features with the objective function. Hence, it tries to
push the coefficients for many variables to zero and hence reduce the cost term. This helps to
reduce model complexity so that the model can become better at predicting (generalizing).
Q56 What is Linear Regression?
Linear Regression is a supervised Machine Learning algorithm. It is used to find the linear
relationship between the dependent and the independent variables for predictive analysis.
Q57 What is the Variance Inflation Factor?
Variance Inflation Factor (VIF) is the estimate of the volume of multicollinearity in a collection of
many regression variables.
VIF = Variance of the model / Variance of the model with a single independent variable
We have to calculate this ratio for every independent variable. If VIF is high, then it shows the
high collinearity of the independent variables.
Q58 We know that one hot encoding increases the dimensionality of a dataset,
but label encoding doesn’t. How?
When we use one-hot encoding, there is an increase in the dimensionality of a dataset. The
reason for the increase in dimensionality is that, for every class in the categorical variables, it
forms a different variable.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Q59 What is a Decision Tree?
A decision tree is used to explain the sequence of actions that must be performed to get the
desired output. It is a hierarchical diagram that shows the actions.
Q60 What is the Binarizing of data? How to Binarize?
In most of the Machine Learning Interviews, apart from theoretical questions, interviewers focus
on the implementation part. So, this ML Interview Questions focused on the implementation of
the theoretical concepts.
Converting data into binary values on the basis of threshold values is known as the binarizing of
data. The values that are less than the threshold are set to 0 and the values that are greater
than the threshold are set to 1. This process is useful when we have to perform feature
engineering, and we can also use it for adding unique features.
Q61 What is cross-validation?
Cross-validation is essentially a technique used to assess how well a model performs on a new
independent dataset. The simplest example of cross-validation is when you split your data into
two groups: training data and testing data, where you use the training data to build the model
and the testing data to test the model.
Q62 When would you use random forests Vs SVM and why?
There are a couple of reasons why a random forest is a better choice of the model than a
support vector machine:
● Random forests allow you to determine the feature importance. SVM’s can’t do this.
● Random forests are much quicker and simpler to build than an SVM.
● For multi-class classification problems, SVMs require a one-vs-rest method, which is
less scalable and more memory intensive.
Q63 What are the drawbacks of a linear model?
There are a couple of drawbacks of a linear model:
● A linear model holds some strong assumptions that may not be true in the application. It
assumes a linear relationship, multivariate normality, no or little multicollinearity, no
auto-correlation, and homoscedasticity
● A linear model can’t be used for discrete or binary outcomes.
● You can’t vary the model flexibility of a linear model.
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Q64 Do you think 50 small decision trees are better than a large one? Why?
Another way of asking this question is “Is a random forest a better model than a decision tree?”
And the answer is yes because a random forest is an ensemble method that takes many weak
decision trees to make a strong learner. Random forests are more accurate, more robust, and
less prone to overfitting.
Q65 What is a kernel? Explain the kernel trick
A kernel is a way of computing the dot product of two vectors x and y in some (possibly very
high dimensional) feature space, which is why kernel functions are sometimes called
“generalized dot product”
The kernel trick is a method of using a linear classifier to solve a non-linear problem by
transforming linearly inseparable data to linearly separable ones in a higher dimension.
Q66 State the differences between causality and correlation?
Causality applies to situations where one action, say X, causes an outcome, say Y, whereas
Correlation is just relating one action (X) to another action(Y) but X does not necessarily cause
Y.
Q67 What is the exploding gradient problem while using the backpropagation
technique?
When large error gradients accumulate and result in large changes in the neural network
weights during training, it is called the exploding gradient problem. The values of weights can
become so large as to overflow and result in NaN values. This makes the model unstable and
the learning of the model to stall just like the vanishing gradient problem.
Q68 What do you mean by Associative Rule Mining (ARM)?
Associative Rule Mining is one of the techniques to discover patterns in data like features
(dimensions) which occur together and features (dimensions) which are correlated.
Q69 What is Marginalisation? Explain the process.
Marginalizationarginalisation is summing the probability of a random variable X given the joint
probability distribution of X with other variables. It is an application of the law of total probability.
Q70 Why is the rotation of components so important in Principle Component
Analysis(PCA)?
Rotation in PCA is very important as it maximizes the separation within the variance obtained by
all the components because of which interpretation of components would become easier. If the
components are not rotated, then we need extended components to describe the variance of
the components.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Q71 What is the difference between regularization and normalisation?
Normalisation adjusts the data; regularisation adjusts the prediction function. If your data is on
very different scales (especially low to high), you would want to normalise the data. Alter each
column to have compatible basic statistics. This can be helpful to make sure there is no loss of
accuracy. One of the goals of model training is to identify the signal and ignore the noise if the
model is given free rein to minimize error, there is a possibility of suffering from overfitting.
Regularization imposes some control on this by providing simpler fitting functions over complex
ones.
Q72 When does the linear regression line stop rotating or finds an optimal spot where it
is fitted on data?
A place where the highest RSquared value is found, is the place where the line comes to rest.
RSquared represents the amount of variance captured by the virtual linear regression line with
respect to the total variance captured by the dataset.
Q73 How does the SVM algorithm deal with self-learning?
SVM has a learning rate and expansion rate which takes care of this. The learning rate
compensates or penalises the hyperplanes for making all the wrong moves and expansion rate
deals with finding the maximum separation area between classes.
Q74 How do you handle outliers in the data?
Outlier is an observation in the data set that is far away from other observations in the data set.
We can discover outliers using tools and functions like box plot, scatter plot, Z-Score, IQR score
etc. and then handle them based on the visualization we have got. To handle outliers, we can
cap at some threshold, use transformations to reduce skewness of the data and remove outliers
if they are anomalies or errors.
Q75 Name and define techniques used to find similarities in the recommendation system.
Pearson correlation and Cosine correlation are techniques used to find similarities in
recommendation systems.
Q76 Why would you Prune your tree?
In the context of data science or AIML, pruning refers to the process of reducing redundant
branches of a decision tree. Decision Trees are prone to overfitting, pruning the tree helps to
reduce the size and minimizes the chances of overfitting. Pruning involves turning branches of a
decision tree into leaf nodes and removing the leaf nodes from the original branch. It serves as
a tool to perform the tradeoff.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Q77 Mention some of the EDA Techniques?
Exploratory Data Analysis (EDA) helps analysts to understand the data better and forms the
foundation of better models.
Visualization
● Univariate visualization
● Bivariate visualization
● Multivariate visualization
Missing Value Treatment – Replace missing values with Either Mean/Median
Outlier Detection – Use Boxplot to identify the distribution of Outliers, then Apply IQR to set the
boundary for IQR
Q78 What is data augmentation? Can you give some examples?
Data augmentation is a technique for synthesizing new data by modifying existing data in such a
way that the target is not changed, or it is changed in a known way.
CV is one of the fields where data augmentation is very useful. There are many modifications
that we can do to images:
● Resize
● Horizontal or vertical flip
● Rotate
● Add noise
● Deform
● Modify colors
Each problem needs a customized data augmentation pipeline. For example, on OCR, doing
flips will change the text and won’t be beneficial; however, resizes and small rotations may help.
Q79 What is Inductive Logic Programming in Machine Learning (ILP)?
Inductive Logic Programming (ILP) is a subfield of machine learning which uses logic
programming representing background knowledge and examples.
Q80 What is the difference between inductive machine learning and deductive machine
learning?
The difference between inductive machine learning and deductive machine learning are as
follows: machine-learning where the model learns by examples from a set of observed
instances to draw a generalized conclusion whereas in deductive learning the model first draws
the conclusion and then the conclusion is drawn.
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Q81 Difference between machine learning and deep learning
Machine learning is a branch of computer science and a method to implement artificial
intelligence. This technique provides the ability to automatically learn and improve from
experiences without being explicitly programmed.
Deep learning can be said as a subset of machine learning. It is mainly based on the artificial
neural network where data is taken as an input and the technique makes intuitive decisions
using the artificial neural network.
Q82 What Are The Steps Involved In Machine Learning Project?
As you plan for doing a machine learning project. There are several important steps you must
follow to achieve a good working model and they are data collection, data preparation, choosing
a machine learning model, training the model, model evaluation, parameter tuning and lastly
prediction.
Q83 Differences between Artificial Intelligence and Machine Learning?
Artificial intelligence is a broader prospect than machine learning. Artificial intelligence mimics
the cognitive functions of the human brain. The purpose of AI is to carry out a task in an
intelligent manner based on algorithms. On the other hand, machine learning is a subclass of
artificial intelligence. To develop an autonomous machine in such a way so that it can learn
without being explicitly programmed is the goal of machine learning.
Q84 Steps Needed to Choose the Appropriate Machine Learning Algorithm for
your Classification problem.
Firstly, you need to have a clear picture of your data, your constraints, and your problems
before heading towards different machine learning algorithms. Secondly, you have to
understand which type and kind of data you have because it plays a primary role in deciding
which algorithm you have to use.
Following this step is the data categorization step, which is a two-step process – categorization
by input and categorization by output. The next step is to understand your constraints; that is,
what is your data storage capacity? How fast the prediction has to be? etc.
Finally, find the available machine learning algorithms and implement them wisely. Along with
that, also try to optimize the hyperparameters which can be done in three ways – grid search,
random search, and Bayesian optimization.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Q85 Explain Backpropagation in Machine Learning.
A very important question for your machine learning interview. Backpropagation is the
algorithm for computing artificial neural networks (ANN). It is used by the gradient descent
optimization that exploits the chain rule. By calculating the gradient of the loss function, the
weight of the neurons is adjusted to a certain value. To train a multi-layered neural network is
the prime motivation of backpropagation so that it can learn the appropriate internal
demonstrations. This will help them learn to map any input to its respective output arbitrarily.
Q86 What is the Convex Function?
This question is very often asked in machine learning interviews. A convex function is a
continuous function, and the value of the midpoint at every interval in its given domain is less
than the numerical mean of the values at the two ends of the interval.
Q87 What’s the Relationship between True Positive Rate and Recall?
The True positive rate in machine learning is the percentage of the positives that have been
properly acknowledged, and recall is just the count of the results that have been correctly
identified and are relevant. Therefore, they are the same things, just having different names. It is
also known as sensitivity.
Q88 List some Tools for Parallelizing Machine Learning Algorithms.
Although this question may seem very easy, make sure not to skip this one because it is also
very closely related to artificial intelligence and thereby, AI interview questions. Almost all
machine learning algorithms are easy to serialize. Some of the basic tools for parallelizing are
Matlab, Weka, R, Octave, or the Python-based sci-kit learn.
Q89 What do you mean by Genetic Programming?
Genetic Programming (GP) is almost similar to an Evolutionary Algorithm, a subset of machine
learning. Genetic programming software systems implement an algorithm that uses random
mutation, a fitness function, crossover, and multiple generations of evolution to resolve a
user-defined task. The genetic programming model is based on testing and choosing the best
option among a set of results.
Q90 What do you know about Bayesian Networks?
Bayesian Networks also referred to as 'belief networks' or 'casual networks', are used to
represent the graphical model for probability relationship among a set of variables.
For example, a Bayesian network can be used to represent the probabilistic relationships
between diseases and symptoms. As per the symptoms, the network can also compute the
probabilities of the presence of various diseases.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Efficient algorithms can perform inference or learning in Bayesian networks. Bayesian networks
which relate the variables (e.g., speech signals or protein sequences) are called dynamic
Bayesian networks.
Q91 Which are the two components of the Bayesian logic program?
A Bayesian logic program consists of two components:
● Logical It contains a set of Bayesian Clauses, which capture the qualitative structure of
the domain.
● Quantitative It is used to encode quantitative information about the domain.
Q92 How is machine learning used in day-to-day life?
Most of the people are already using machine learning in their everyday life. Assume that you
are engaging with the internet, you are actually expressing your preferences, likes, dislikes
through your searches. All these things are picked up by cookies coming on your computer,
from this, the behavior of a user is evaluated. It helps to increase the progress of a user through
the internet and provide similar suggestions.
The navigation system can also be considered as one of the examples where we are using
machine learning to calculate a distance between two places using optimization techniques.
Surely, people are going to more engage with machine learning in the near future
Q93 Define Sampling. Why do we need it?
Answer: Sampling is a process of choosing a subset from a target population that would serve
as its representative. We use the data from the sample to understand the pattern in the
community as a whole. Sampling is necessary because often, we can not gather or process the
complete data within a reasonable time.
Q94 What does the term decision boundary mean?
Answer: A decision boundary or a decision surface is a hypersurface which divides the
underlying feature space into two subspaces, one for each class. If the decision boundary is a
hyperplane, then the classes are linearly separable.
Q95 Define entropy?
Answer: Entropy is the measure of uncertainty associated with random variable Y. It is the
expected number of bits required to communicate the value of the variable.
Q96 Indicate the top intents of machine learning?
Answer: The top intents of machine learning are stated below,
● The system gets information from the already established computations to give
well-founded decisions and outputs.
● It locates certain patterns in the data and then makes certain predictions on it to provide
answers on matters.
Steve Nouri https://www.linkedin.com/in/stevenouri/
Q97 Highlight the differences between the Generative model and the
Discriminative model?
The aim of the Generative model is to generate new samples from the same distribution and
new data instances, Whereas, the Discriminative model highlights the differences between
different kinds of data instances. It tries to learn directly from the data and then classifies the
data.
Q98 Identify the most important aptitudes of a machine learning engineer?
Machine learning allows the computer to learn itself without being decidedly programmed. It
helps the system to learn from experience and then improve from its mistakes. The intelligence
system, which is based on machine learning, can learn from recorded data and past incidents.
In-depth knowledge of statistics, probability, data modelling, programming language, as well as
CS, Application of ML Libraries and algorithms, and software design is required to become a
successful machine learning engineer.
Q99 What is feature engineering? How do you apply it in the process of
modelling?
Feature engineering is the process of transforming raw data into features that better represent
the underlying problem to the predictive models, resulting in improved model accuracy on
unseen data.
Q100 How can learning curves help create a better model?
Learning curves give the indication of the presence of overfitting or underfitting.
In a learning curve, the training error and cross-validating error are plotted against the number
of training data points.
References
1 springboard.com 2 simplilearn.com 3 geeksforgeeks.org 4 elitedatascience.com 5
analyticsvidhya.com 6 guru99.com 7 intellipaat.com 8 towardsdatascience.com 9
mygreatlearning.com 10 mindmajix.com 11 toptal.com 12 glassdoor.co.in 13 udacity.com 14
educba.com 15 analyticsindiamag.com 16 ubuntupit.com 17 javatpoint.com 18 quora.com 19
hackr.io 20 kaggle.com
Steve Nouri https://www.linkedin.com/in/stevenouri/
Steve Nouri https://www.linkedin.com/in/stevenouri/
Top 100 NLP Questions
Steve Nouri
Q1. Which of the following techniques can be used for keyword normalization in
NLP, the process of converting a keyword into its base form?
a. Lemmatization
b. Soundex
c. Cosine Similarity
d. N-grams
Answer : a) Lemmatization helps to get to the base form of a word, e.g. are playing -> play, eating
-> eat, etc.Other options are meant for different purposes.
Q2. Which of the following techniques can be used to compute the distance
between two word vectors in NLP?
a. Lemmatization
b. Euclidean distance
c. Cosine Similarity
d. N-grams
Answer : b) and c)
Distance between two word vectors can be computed using Cosine similarity and Euclidean
Distance. Cosine Similarity establishes a cosine angle between the vector of two words. A cosine
angle close to each other between two word vectors indicates the words are similar and vice a
versa.
E.g. cosine angle between two words “Football” and “Cricket” will be closer to 1 as compared to
angle between the words “Football” and “New Delhi”
Q3. What are the possible features of a text corpus in NLP?
a. Count of the word in a document
b. Vector notation of the word
c. Part of Speech Tag
d. Basic Dependency Grammar
e. All of the above
Answer : e)All of the above can be used as features of the text corpus.
Q4. You created a document term matrix on the input data of 20K documents for a
Machine learning model. Which of the following can be used to reduce the
dimensions of data?
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1. Keyword Normalization
2. Latent Semantic Indexing
3. Latent Dirichlet Allocation
a. only 1
b. 2, 3
c. 1, 3
d. 1, 2, 3
Answer : d)
Q5. Which of the text parsing techniques can be used for noun phrase detection,
verb phrase detection, subject detection, and object detection in NLP.
a. Part of speech tagging
b. Skip Gram and N-Gram extraction
c. Continuous Bag of Words
d. Dependency Parsing and Constituency Parsing
Answer : d)
Q6. Dissimilarity between words expressed using cosine similarity will have values
significantly higher than 0.5
a. True
b. False
Answer : a)
Q7. Which one of the following are keyword Normalization techniques in NLP
a. Stemming
b. Part of Speech
c. Named entity recognition
d. Lemmatization
Answer : a) and d)
Part of Speech (POS) and Named Entity Recognition(NER) are not keyword Normalization
techniques. Named Entity help you extract Organization, Time, Date, City, etc..type of entities
from the given sentence, whereas Part of Speech helps you extract Noun, Verb, Pronoun,
adjective, etc..from the given sentence tokens.
Q8. Which of the below are NLP use cases?
a. Detecting objects from an image
b. Facial Recognition
c. Speech Biometric
d. Text Summarization
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Answer : (d)
a) And b) are Computer Vision use cases, and c) is Speech use case.
Only d) Text Summarization is an NLP use case.
Q9. In a corpus of N documents, one randomly chosen document contains a total
of T terms and the term “hello” appears K times.
What is the correct value for the product of TF (term frequency) and IDF (inverse-document-
frequency), if the term “hello” appears in approximately one-third of the total documents?
a. KT * Log(3)
b. T * Log(3) / K
c. K * Log(3) / T
d. Log(3) / KT
Answer : (c)
formula for TF is K/T
formula for IDF is log(total docs / no of docs containing “data”)
= log(1 / (⅓))
= log (3)
Hence correct choice is Klog(3)/T
Q10. In NLP, The algorithm decreases the weight for commonly used words and
increases the weight for words that are not used very much in a collection of
documents
a. Term Frequency (TF)
b. Inverse Document Frequency (IDF)
c. Word2Vec
d. Latent Dirichlet Allocation (LDA)
Answer : b)
Q11. In NLP, The process of removing words like “and”, “is”, “a”, “an”, “the” from
a sentence is called as
a. Stemming
b. Lemmatization
c. Stop word
d. All of the above
Answer : c) In Lemmatization, all the stop words such as a, an, the, etc.. are removed. One can
also define custom stop words for removal.
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Q12. In NLP, The process of converting a sentence or paragraph into tokens is
referred to as Stemming
a. True
b. False
Answer : b) The statement describes the process of tokenization and not stemming, hence it is
False.
Q13. In NLP, Tokens are converted into numbers before giving to any Neural
Network
a. True
b. False
Answer : a) In NLP, all words are converted into a number before feeding to a Neural Network.
Q14 Identify the odd one out
a. nltk
b. scikit learn
c. SpaCy
d. BERT
Answer : d) All the ones mentioned are NLP libraries except BERT, which is a word embedding
Q15 TF-IDF helps you to establish?
a. most frequently occurring word in the document
b. most important word in the document
Answer : b) TF-IDF helps to establish how important a particular word is in the context of the
document corpus. TF-IDF takes into account the number of times the word appears in the
document and offset by the number of documents that appear in the corpus.
● TF is the frequency of term divided by a total number of terms in the document.
● IDF is obtained by dividing the total number of documents by the number of documents
containing the term and then taking the logarithm of that quotient.
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● Tf.idf is then the multiplication of two values TF and IDF.
Q16 In NLP, The process of identifying people, an organization from a given
sentence, paragraph is called
a. Stemming
b. Lemmatization
c. Stop word removal
d. Named entity recognition
Answer : d)
Q17 Which one of the following is not a pre-processing technique in NLP
a. Stemming and Lemmatization
b. converting to lowercase
c. removing punctuations
d. removal of stop words
e. Sentiment analysis
Answer : e) Sentiment Analysis is not a pre-processing technique. It is done after pre-processing
and is an NLP use case. All other listed ones are used as part of statement pre-processing.
Q18 In text mining, converting text into tokens and then converting them into an
integer or floating-point vectors can be done using
a. CountVectorizer
b. TF-IDF
c. Bag of Words
d. NERs
Answer : a) CountVectorizer helps do the above, while others are not applicable.
text =[“Rahul is an avid writer, he enjoys studying understanding and presenting. He loves to
play”]
vectorizer = CountVectorizer()
vectorizer.fit(text)
vector = vectorizer.transform(text)
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print(vector.toarray())
output
[[1 1 1 1 2 1 1 1 1 1 1 1 1 1]]
The second section of the interview questions covers advanced NLP techniques such as
Word2Vec, GloVe word embeddings, and advanced models such as GPT, ELMo, BERT, XLNET
based questions, and explanations.
Q19. In NLP, Words represented as vectors are called as Neural Word Embeddings
a. True
b. False
Answer : a) Word2Vec, GloVe based models build word embedding vectors that are
multidimensional.
Q20. In NLP, Context modeling is supported with which one of the following word
embeddings
1. a. Word2Vec
2. b) GloVe
3. c) BERT
4. d) All of the above
Answer : c) Only BERT (Bidirectional Encoder Representations from Transformer) supports
context modelling where the previous and next sentence context is taken into consideration. In
Word2Vec, GloVe only word embeddings are considered and previous and next sentence context
is not considered.
Q21. In NLP, Bidirectional context is supported by which of the following
embedding
a. Word2Vec
b. BERT
c. GloVe
d. All the above
Answer : b) Only BERT provides a bidirectional context. The BERT model uses the previous and
the next sentence to arrive at the context.Word2Vec and GloVe are word embeddings, they do
not provide any context.
Q22. Which one of the following Word embeddings can be custom trained for a
specific subject in NLP
a. Word2Vec
b. BERT
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c. GloVe
d. All the above
Answer : b) BERT allows Transform Learning on the existing pre-trained models and hence can
be custom trained for the given specific subject, unlike Word2Vec and GloVe where existing word
embeddings can be used, no transfer learning on text is possible.
Q23. Word embeddings capture multiple dimensions of data and are represented
as vectors
a. True
b. False
Answer : a)
Q24. In NLP, Word embedding vectors help establish distance between two tokens
a. True
b. False
Answer : a) One can use Cosine similarity to establish distance between two vectors represented
through Word Embeddings
Q25. Language Biases are introduced due to historical data used during training of
word embeddings, which one amongst the below is not an example of bias
a. New Delhi is to India, Beijing is to China
b. Man is to Computer, Woman is to Homemaker
Answer : a)
Statement b) is a bias as it buckets Woman into Homemaker, whereas statement a) is not a
biased statement.
Q26. Which of the following will be a better choice to address NLP use cases such
as semantic similarity, reading comprehension, and common sense reasoning
a. ELMo
b. Open AI’s GPT
c. ULMFit
Answer : b) Open AI’s GPT is able to learn complex pattern in data by using the Transformer
models Attention mechanism and hence is more suited for complex use cases such as semantic
similarity, reading comprehensions, and common sense reasoning.
Q27. Transformer architecture was first introduced with?
a. GloVe
b. BERT
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c. Open AI’s GPT
d. ULMFit
Answer : c) ULMFit has an LSTM based Language modeling architecture. This got replaced into
Transformer architecture with Open AI’s GPT
Q28. Which of the following architecture can be trained faster and needs less
amount of training data
a. LSTM based Language Modelling
b. Transformer architecture
Answer : b) Transformer architectures were supported from GPT onwards and were faster to
train and needed less amount of data for training too.
Q29. Same word can have multiple word embeddings possible with ____________?
a. GloVe
b. Word2Vec
c. ELMo
d. nltk
Answer : c) EMLo word embeddings supports same word with multiple embeddings, this helps
in using the same word in a different context and thus captures the context than just meaning of
the word unlike in GloVe and Word2Vec. Nltk is not a word embedding.
Q30 For a given token, its input representation is the sum of embedding from the
token, segment and position embedding
a. ELMo
b. GPT
c. BERT
d. ULMFit
Answer : c) BERT uses token, segment and position embedding.
Q31. Trains two independent LSTM language model left to right and right to left and
shallowly concatenates them
a. GPT
b. BERT
c. ULMFit
d. ELMo
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Answer : d) ELMo tries to train two independent LSTM language models (left to right and right to
left) and concatenates the results to produce word embedding.
Q32. Uses unidirectional language model for producing word embedding
a. BERT
b. GPT
c. ELMo
d. Word2Vec
Answer : b) GPT is a unidirectional model and word embedding are produced by training on
information flow from left to right. ELMo is bidirectional but shallow. Word2Vec provides simple
word embedding.
Q33. In this architecture, the relationship between all words in a sentence is
modelled irrespective of their position. Which architecture is this?
a. OpenAI GPT
b. ELMo
c. BERT
d. ULMFit
Answer : c)BERT Transformer architecture models the relationship between each word and all
other words in the sentence to generate attention scores. These attention scores are later used
as weights for a weighted average of all words’ representations which is fed into a fully-connected
network to generate a new representation.
Q34. List 10 use cases to be solved using NLP techniques?
● Sentiment Analysis
● Language Translation (English to German, Chinese to English, etc..)
● Document Summarization
● Question Answering
● Sentence Completion
● Attribute extraction (Key information extraction from the documents)
● Chatbot interactions
● Topic classification
● Intent extraction
● Grammar or Sentence correction
● Image captioning
● Document Ranking
● Natural Language inference
Q35. Transformer model pays attention to the most important word in Sentence
a. True
b. False
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Answer : a) Attention mechanisms in the Transformer model are used to model the relationship
between all words and also provide weights to the most important word.
Q36. Which NLP model gives the best accuracy amongst the following?
a. BERT
b. XLNET
c. GPT-2
d. ELMo
Answer : b) XLNET has given best accuracy amongst all the models. It has outperformed BERT
on 20 tasks and achieves state of art results on 18 tasks including sentiment analysis, question
answering, natural language inference, etc.
Q37. Permutation Language models is a feature of
a. BERT
b. EMMo
c. GPT
d. XLNET
Answer : d) XLNET provides permutation-based language modelling and is a key difference from
BERT. In permutation language modeling, tokens are predicted in a random manner and not
sequential. The order of prediction is not necessarily left to right and can be right to left. The
original order of words is not changed but a prediction can be random.
The conceptual difference between BERT and XLNET can be seen from the following diagram.
Q38. Transformer XL uses relative positional embedding
a. True
b. False
a) Instead of embedding having to represent the absolute position of a word, Transformer XL uses
an embedding to encode the relative distance between the words. This embedding is used to
compute the attention score between any 2 words that could be separated by n words before or
after.
Q39. What is Naive Bayes algorithm, When we can use this algorithm in NLP?
Naive Bayes algorithm is a collection of classifiers which works on the principles of the Bayes’
theorem. This series of NLP model forms a family of algorithms that can be used for a wide range
of classification tasks including sentiment prediction, filtering of spam, classifying documents and
more.
Naive Bayes algorithm converges faster and requires less training data. Compared to other
discriminative models like logistic regression, Naive Bayes model it takes lesser time to train. This
algorithm is perfect for use while working with multiple classes and text classification where the
data is dynamic and changes frequently.
Q40. Explain Dependency Parsing in NLP?
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Dependency Parsing, also known as Syntactic parsing in NLP is a process of assigning syntactic
structure to a sentence and identifying its dependency parses. This process is crucial to
understand the correlations between the “head” words in the syntactic structure.
The process of dependency parsing can be a little complex considering how any sentence can
have more than one dependency parses. Multiple parse trees are known as ambiguities.
Dependency parsing needs to resolve these ambiguities in order to effectively assign a syntactic
structure to a sentence.
Dependency parsing can be used in the semantic analysis of a sentence apart from the syntactic
structuring.
Q41. What is text Summarization?
Text summarization is the process of shortening a long piece of text with its meaning and effect
intact. Text summarization intends to create a summary of any given piece of text and outlines
the main points of the document. This technique has improved in recent times and is capable of
summarizing volumes of text successfully.
Text summarization has proved to a blessing since machines can summarise large volumes of
text in no time which would otherwise be really time-consuming. There are two types of text
summarization:
● Extraction-based summarization
● Abstraction-based summarization
Q42. What is NLTK? How is it different from Spacy?
NLTK or Natural Language Toolkit is a series of libraries and programs that are used for symbolic
and statistical natural language processing. This toolkit contains some of the most powerful
libraries that can work on different ML techniques to break down and understand human
language. NLTK is used for Lemmatization, Punctuation, Character count, Tokenization, and
Stemming. The difference between NLTK and Spacey are as follows:
● While NLTK has a collection of programs to choose from, Spacey contains only the best-
suited algorithm for a problem in its toolkit
● NLTK supports a wider range of languages compared to Spacey (Spacey supports only 7
languages)
● While Spacey has an object-oriented library, NLTK has a string processing library
● Spacey can support word vectors while NLTK cannot
Q43. What is information extraction?
Information extraction in the context of Natural Language Processing refers to the technique of
extracting structured information automatically from unstructured sources to ascribe meaning to
it. This can include extracting information regarding attributes of entities, relationship between
different entities and more. The various models of information extraction includes:
● Tagger Module
● Relation Extraction Module
● Fact Extraction Module
● Entity Extraction Module
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● Sentiment Analysis Module
● Network Graph Module
● Document Classification & Language Modeling Module
Q44. What is Bag of Words?
Bag of Words is a commonly used model that depends on word frequencies or occurrences to
train a classifier. This model creates an occurrence matrix for documents or sentences
irrespective of its grammatical structure or word order.
Q45. What is Pragmatic Ambiguity in NLP?
Pragmatic ambiguity refers to those words which have more than one meaning and their use in
any sentence can depend entirely on the context. Pragmatic ambiguity can result in multiple
interpretations of the same sentence. More often than not, we come across sentences which have
words with multiple meanings, making the sentence open to interpretation. This multiple
interpretation causes ambiguity and is known as Pragmatic ambiguity in NLP.
Q46. What is a Masked Language Model?
Masked language models help learners to understand deep representations in downstream tasks
by taking an output from the corrupt input. This model is often used to predict the words to be
used in a sentence.
Q48. What are the best NLP Tools?
Some of the best NLP tools from open sources are:
● SpaCy
● TextBlob
● Textacy
● Natural language Toolkit
● Retext
● NLP.js
● Stanford NLP
● CogcompNLP
Q49. What is POS tagging?
Parts of speech tagging better known as POS tagging refers to the process of identifying specific
words in a document and group them as part of speech, based on its context. POS tagging is also
known as grammatical tagging since it involves understanding grammatical structures and
identifying the respective component.
POS tagging is a complicated process since the same word can be different parts of speech
depending on the context. The same generic process used for word mapping is quite ineffective
for POS tagging because of the same reason.
Q50. What is NES?
Name entity recognition is more commonly known as NER is the process of identifying specific
entities in a text document which are more informative and have a unique context. These often
denote places, people, organisations, and more. Even though it seems like these entities are
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proper nouns, the NER process is far from identifying just the nouns. In fact, NER involves entity
chunking or extraction wherein entities are segmented to categorise them under different
predefined classes. This step further helps in extracting information.
Q51 Explain the Masked Language Model?
Masked language modelling is the process in which the output is taken from the corrupted input.
This model helps the learners to master the deep representations in downstream tasks. You can
predict a word from the other words of the sentence using this model.
Q52 What is pragmatic analysis in NLP?
Pragmatic Analysis: It deals with outside word knowledge, which means knowledge that is
external to the documents and/or queries. Pragmatics analysis that focuses on what was
described is reinterpreted by what it actually meant, deriving the various aspects of language that
require real-world knowledge.
Q53 What is perplexity in NLP?
The word "perplexed" means "puzzled" or "confused", thus Perplexity in general means the
inability to tackle something complicated and a problem that is not specified. Therefore, Perplexity
in NLP is a way to determine the extent of uncertainty in predicting some text.
In NLP, perplexity is a way of evaluating language models. Perplexity can be high and low; Low
perplexity is ethical because the inability to deal with any complicated problem is less while high
perplexity is terrible because the failure to deal with a complicated is high.
Q54 What is ngram in NLP?
N-gram in NLP is simply a sequence of n words, and we also conclude the sentences which
appeared more frequently, for example, let us consider the progression of these three words:
● New York (2 gram)
● The Golden Compass (3 gram)
● She was there in the hotel (4 gram)
Now from the above sequence, we can easily conclude that sentence (a) appeared more
frequently than the other two sentences, and the last sentence(c) is not seen that often. Now if
we assign probability in the occurrence of an n-gram, then it will be advantageous. It would help
in making next-word predictions and in spelling error corrections.
Q55 Explain differences between AI, Machine Learning and NLP
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Q56 Why self-attention is awesome?
“In terms of computational complexity, self-attention layers are faster than recurrent layers when
the sequence length n is smaller than the representation dimensionality d, which is most often the
case with sentence representations used by state-of-the-art models in machine translations, such
as word-piece and byte-pair representations.” — from Attention is all you need
Q57 What are stop words?
Stop words are said to be useless data for a search engine. Words such as articles, prepositions,
etc. are considered as stop words. There are stop words such as was, were, is, am, the, a, an,
how, why, and many more. In Natural Language Processing, we eliminate the stop words to
understand and analyze the meaning of a sentence. The removal of stop words is one of the most
important tasks for search engines. Engineers design the algorithms of search engines in such a
way that they ignore the use of stop words. This helps show the relevant search result for a query.
Q58 What is Latent Semantic Indexing (LSI)?
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Latent semantic indexing is a mathematical technique used to improve the accuracy of the
information retrieval process. The design of LSI algorithms allows machines to detect the hidden
(latent) correlation between semantics (words). To enhance information understanding, machines
generate various concepts that associate with the words of a sentence.
The technique used for information understanding is called singular value decomposition. It is
generally used to handle static and unstructured data. The matrix obtained for singular value
decomposition contains rows for words and columns for documents. This method best suits to
identify components and group them according to their types.
The main principle behind LSI is that words carry a similar meaning when used in a similar context.
Computational LSI models are slow in comparison to other models. However, they are good at
contextual awareness that helps improve the analysis and understanding of a text or a document.
Q60 What are Regular Expressions?
A regular expression is used to match and tag words. It consists of a series of characters for
matching strings.
Suppose, if A and B are regular expressions, then the following are true for them:
● If {ɛ} is a regular language, then ɛ is a regular expression for it.
● If A and B are regular expressions, then A + B is also a regular expression within the
language {A, B}.
● If A and B are regular expressions, then the concatenation of A and B (A.B) is a regular
expression.
● If A is a regular expression, then A* (A occurring multiple times) is also a regular
expression.
Q61 What are unigrams, bigrams, trigrams, and n-grams in NLP?
When we parse a sentence one word at a time, then it is called a unigram. The sentence parsed
two words at a time is a bigram.
When the sentence is parsed three words at a time, then it is a trigram. Similarly, n-gram refers
to the parsing of n words at a time.
Example: To understand unigrams, bigrams, and trigrams, you can refer to the below diagram:
Q62 What are the steps involved in solving an NLP problem?
Below are the steps involved in solving an NLP problem:
1. Gather the text from the available dataset or by web scraping
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2. Apply stemming and lemmatization for text cleaning
3. Apply feature engineering techniques
4. Embed using word2vec
5. Train the built model using neural networks or other Machine Learning techniques
6. Evaluate the model’s performance
7. Make appropriate changes in the model
8. Deploy the model
Q63. There have some various common elements of natural language processing.
Those elements are very important for understanding NLP properly, can you please
explain the same in details with an example?
Answer:
There have a lot of components normally using by natural language processing (NLP). Some of
the major components are explained below:
● Extraction of Entity: It actually identifying and extracting some critical data from the
available information which help to segmentation of provided sentence on identifying each
entity. It can help in identifying one human that it’s fictional or real, same kind of reality
identification for any organization, events or any geographic location etc.
● The analysis in a syntactic way: it mainly helps for maintaining ordering properly of the
available words.
Q64 In the case of processing natural language, we normally mentioned one
common terminology NLP and binding every language with the same terminology
properly. Please explain in details about this NLP terminology with an example?
Answer:
This is the basic NLP Interview Questions asked in an interview. There have some several factors
available in case of explaining natural language processing. Some of the key factors are given
below:
● Vectors and Weights: Google Word vectors, length of TF-IDF, varieties documents, word
vectors, TF-IDF.
● Structure of Text: Named Entities, tagging of part of speech, identifying the head of the
sentence.
● Analysis of sentiment: Know about the features of sentiment, entities available for the
sentiment, sentiment common dictionary.
● Classification of Text: Learning supervising, set off a train, set of validation in Dev, Set of
define test, a feature of the individual text, LDA.
● Reading of Machine Language: Extraction of the possible entity, linking with an individual
entity, DBpedia, some libraries like Pikes or FRED.
Q65 Explain briefly about word2vec
Word2Vec embeds words in a lower-dimensional vector space using a shallow neural network.
The result is a set of word-vectors where vectors close together in vector space have similar
meanings based on context, and word-vectors distant to each other have differing meanings. For
example, apple and orange would be close together and apple and gravity would be relatively far.
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There are two versions of this model based on skip-grams (SG) and continuous-bag-of-words
(CBOW).
Q66 What are the metrics used to test an NLP model?
Accuracy, Precision, Recall and F1. Accuracy is the usual ratio of the prediction to the desired
output. But going just be accuracy is naive considering the complexities involved.
Q67 What are some ways we can preprocess text input?
Here are several preprocessing steps that are commonly used for NLP tasks:
● case normalization: we can convert all input to the same case (lowercase or uppercase)
as a way of reducing our text to a more canonical form
● punctuation/stop word/white space/special characters removal: if we don’t think these
words or characters are relevant, we can remove them to reduce the feature space
● lemmatizing/stemming: we can also reduce words to their inflectional forms (i.e. walks →
walk) to further trim our vocabulary
● generalizing irrelevant information: we can replace all numbers with a <NUMBER> token
or all names with a <NAME> token
Q68 How does the encoder-decoder structure work for language modelling?
The encoder-decoder structure is a deep learning model architecture responsible for several state
of the art solutions, including Machine Translation.
The input sequence is passed to the encoder where it is transformed to a fixed-dimensional vector
representation using a neural network. The transformed input is then decoded using another
neural network. Then, these outputs undergo another transformation and a softmax layer. The
final output is a vector of probabilities over the vocabularies. Meaningful information is extracted
based on these probabilities.
Q69 What are attention mechanisms and why do we use them?
This was a followup to the encoder-decoder question. Only the output from the last time step is
passed to the decoder, resulting in a loss of information learned at previous time steps. This
information loss is compounded for longer text sequences with more time steps.
Attention mechanisms are a function of the hidden weights at each time step. When we use
attention in encoder-decoder networks, the fixed-dimensional vector passed to the decoder
becomes a function of all vectors outputted in the intermediary steps.
Two commonly used attention mechanisms are additive attention and multiplicative attention. As
the names suggest, additive attention is a weighted sum while multiplicative attention is a
weighted multiplier of the hidden weights. During the training process, the model also learns
weights for the attention mechanisms to recognize the relative importance of each time step.
Q70 How would you implement an NLP system as a service, and what are some
pitfalls you might face in production?
This is less of a NLP question than a question for productionizing machine learning models. There
are however certain intricacies to NLP models.
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Without diving too much into the productionization aspect, an ideal Machine Learning service will
have:
● endpoint(s) that other business systems can use to make inference
● a feedback mechanism for validating model predictions
● a database to store predictions and ground truths from the feedback
● a workflow orchestrator which will (upon some signal) re-train and load the new model for
serving based on the records from the database + any prior training data
● some form of model version control to facilitate rollbacks in case of bad deployments
● post-production accuracy and error monitoring
Q71 How can we handle misspellings for text input?
By using word embeddings trained over a large corpus (for instance, an extensive web scrape of
billions of words), the model vocabulary would include common misspellings by design. The
model can then learn the relationship between misspelled and correctly spelled words to
recognize their semantic similarity.
We can also preprocess the input to prevent misspellings. Terms not found in the model
vocabulary can be mapped to the “closest” vocabulary term using:
● edit distance between strings
● phonetic distance between word pronunciations
● keyword distance to catch common typos
Q72 Which of the following models can perform tweet classification with regards
to context mentioned above?
A) Naive Bayes
B) SVM
C) None of the above
Solution: (C)
Since, you are given only the data of tweets and no other information, which means there is no
target variable present. One cannot train a supervised learning model, both svm and naive bayes
are supervised learning techniques.
Q73 You have created a document term matrix of the data, treating every tweet as
one document. Which of the following is correct, in regards to document term
matrix?
1. Removal of stopwords from the data will affect the dimensionality of data
2. Normalization of words in the data will reduce the dimensionality of data
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3. Converting all the words in lowercase will not affect the dimensionality of the data
A) Only 1
B) Only 2
C) Only 3
D) 1 and 2
E) 2 and 3
F) 1, 2 and 3
Solution: (D)
Choices A and B are correct because stopword removal will decrease the number of features in
the matrix, normalization of words will also reduce redundant features, and, converting all words
to lowercase will also decrease the dimensionality.
Q74 Which of the following features can be used for accuracy improvement of a
classification model?
A) Frequency count of terms
B) Vector Notation of sentence
C) Part of Speech Tag
D) Dependency Grammar
E) All of these
Solution: (E)
All of the techniques can be used for the purpose of engineering features in a model.
Q75 What percentage of the total statements are correct with regards to Topic
Modeling?
1. It is a supervised learning technique
2. LDA (Linear Discriminant Analysis) can be used to perform topic modeling
3. Selection of number of topics in a model does not depend on the size of data
4. Number of topic terms are directly proportional to size of the data
A) 0
B) 25
C) 50
D) 75
E) 100
Solution: (A)
LDA is unsupervised learning model, LDA is latent Dirichlet allocation, not Linear discriminant
analysis. Selection of the number of topics is directly proportional to the size of the data, while
number of topic terms is not directly proportional to the size of the data. Hence none of the
statements are correct.
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Q76 In Latent Dirichlet Allocation model for text classification purposes, what does
alpha and beta hyperparameter represent-
A) Alpha: number of topics within documents, beta: number of terms within topics False
B) Alpha: density of terms generated within topics, beta: density of topics generated within terms
False
C) Alpha: number of topics within documents, beta: number of terms within topics False
D) Alpha: density of topics generated within documents, beta: density of terms generated within
topics True
Solution: (D)
Option D is correct
Q77 What is the problem with ReLu?
● Exploding gradient(Solved by gradient clipping)
● Dying ReLu — No learning if the activation is 0 (Solved by parametric relu)
● Mean and variance of activations is not 0 and 1.(Partially solved by subtracting around 0.5
from activation. Better explained in fastai videos)
Q78 What is the difference between learning latent features using SVD and getting
embedding vectors using deep network?
SVD uses linear combination of inputs while a neural network uses nonlinear combination.
Q79 What is the information in the hidden and cell state of LSTM?
Hidden stores all the information till that time step and cell state stores particular information that
might be needed in the future time step.
Number of parameters in an LSTM model with bias
4(h+h²+h) where is input vectors size and h is output vectors size a.k.a. hidden
The point to see here is that mh dictates the model size as m>>h. Hence it's important to have a
small vocab.
Time complexity of LSTM
seq_length*hidden²
Time complexity of transfomer
seq_length²*hidden
When hidden size is more than the seq_length(which is normally the case), transfomer is faster
than LSTM.
Q80 When is self-attention not faster than recurrent layers?
When the sequence length is greater than the representation dimensions. This is rare.
Q81 What is the benefit of learning rate warm-up?
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Learning rate warm-up is a learning rate schedule where you have low (or lower) learning rate at
the beginning of training to avoid divergence due to unreliable gradients at the beginning. As the
model becomes more stable, the learning rate would increase to speed up convergence.
Q82 What’s the difference between hard and soft parameter sharing in multi-task
learning?
Hard sharing is where we train for all the task at the same time and update our weights using all
the losses whereas soft sharing is where we train for one task at a time.
Q83 What’s the difference between BatchNorm and LayerNorm?
BatchNorm computes the mean and variance at each layer for every minibatch whereas
LayerNorm computes the mean and variance for every sample for each layer independently.
Batch normalisation allows you to set higher learning rates, increasing speed of training as it
reduces the unstability of initial starting weights.
Q84 Difference between BatchNorm and LayerNorm?
BatchNorm — Compute the mean and var at each layer for every minibatch
LayerNorm — Compute the mean and var for every single sample for each layer independently
Q85 Why does the transformer block have LayerNorm instead of BatchNorm?
Looking at the advantages of LayerNorm, it is robust to batch size and works better as it works at
the sample level and not batch level.
Q86 What changes would you make to your deep learning code if you knew there
are errors in your training data?
We can do label smoothening where the smoothening value is based on % error. If any particular
class has known error, we can also use class weights to modify the loss.
Q87 What are the tricks used in ULMFiT? (Not a great questions but checks the
awareness)
● LM tuning with task text
● Weight dropout
● Discriminative learning rates for layers
● Gradual unfreezing of layers
● Slanted triangular learning rate schedule
This can be followed up with a question on explaining how they help.
Q88 Tell me a language model which doesn’t use dropout
ALBERT v2 — This throws a light on the fact that a lot of assumptions we take for granted are
not necessarily true. The regularisation effect of parameter sharing in ALBERT is so strong that
dropouts are not needed. (ALBERT v1 had dropouts.)
Q89 What are the differences between GPT and GPT-2? (From Lilian Weng)
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● Layer normalization was moved to the input of each sub-block, similar to a residual unit of
type “building block” (differently from the original type “bottleneck”, it has batch
normalization applied before weight layers).
● An additional layer normalization was added after the final self-attention block.
● A modified initialization was constructed as a function of the model depth.
● The weights of residual layers were initially scaled by a factor of 1/√n where n is the
number of residual layers.
● Use larger vocabulary size and context size.
Q90 What are the differences between GPT and BERT?
● GPT is not bidirectional and has no concept of masking
● BERT adds next sentence prediction task in training and so it also has a segment
embedding
Q91 What are the differences between BERT and ALBERT v2?
● Embedding matrix factorisation(helps in reducing no. of parameters)
● No dropout
● Parameter sharing(helps in reducing no. of parameters and regularisation)
Q92 How does parameter sharing in ALBERT affect the training and inference time?
No effect. Parameter sharing just decreases the number of parameters.
Q93 How would you reduce the inference time of a trained NN model?
● Serve on GPU/TPU/FPGA
● 16 bit quantisation and served on GPU with fp16 support
● Pruning to reduce parameters
● Knowledge distillation (To a smaller transformer model or simple neural network)
● Hierarchical softmax/Adaptive softmax
● You can also cache results as explained here.
Q94 Would you use BPE with classical models?
Of course! BPE is a smart tokeniser and it can help us get a smaller vocabulary which can help
us find a model with less parameters.
Q95 How would you make an arxiv papers search engine? (I was asked — How
would you make a plagiarism detector?)
Get top k results with TF-IDF similarity and then rank results with
● semantic encoding + cosine similarity
● a model trained for ranking
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Q96 Get top k results with TF-IDF similarity and then rank results with
● semantic encoding + cosine similarity
● a model trained for ranking
Q97 How would you make a sentiment classifier?
This is a trick question. The interviewee can say all things such as using transfer learning and
latest models but they need to talk about having a neutral class too otherwise you can have really
good accuracy/f1 and still, the model will classify everything into positive or negative.
The truth is that a lot of news is neutral and so the training needs to have this class. The
interviewee should also talk about how he will create a dataset and his training strategies like the
selection of language model, language model fine-tuning and using various datasets for multi-
task learning.
Q98 What is the difference between regular expression and regular grammar?
A regular expression is the representation of natural language in the form of mathematical
expressions containing a character sequence. On the other hand, regular grammar is the
generator of natural language, defining a set of defined rules and syntax which the strings in the
natural language must follow.
Q99 Why should we use Batch Normalization?
Once the interviewer has asked you about the fundamentals of deep learning architectures, they
would move on to the key topic of improving your deep learning model’s performance.
Batch Normalization is one of the techniques used for reducing the training time of our deep
learning algorithm. Just like normalizing our input helps improve our logistic regression model, we
can normalize the activations of the hidden layers in our deep learning model as well:
Q100 How is backpropagation different in RNN compared to ANN?
In Recurrent Neural Networks, we have an additional loop at each node:
This loop essentially includes a time component into the network as well. This helps in capturing
sequential information from the data, which could not be possible in a generic artificial neural
network.
This is why the backpropagation in RNN is called Backpropagation through Time, as in
backpropagation at each time step.
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Top 100 Questions on Computer Vision
By Steve Nouri
Q1 Which of the following is a challenge when dealing with computer vision
problems?
Variations due to geometric changes (like pose, scale, etc), Variations due to photometric factors
(like illumination, appearance, etc) and Image occlusion. All the above-mentioned options are
challenges in computer vision
Q2 Consider an image with width and height as 100×100. Each pixel in the image
can have a color from Grayscale, i.e. values. How much space would this image
require for storing?
The answer will be 8x100x100 because 8 bits will be required to represent a number from 0-256
Q3 Why do we use convolutions for images rather than just FC layers?
Firstly, convolutions preserve, encode, and actually use the spatial information from the image. If
we used only FC layers we would have no relative spatial information. Secondly, Convolutional
Neural Networks (CNNs) have a partially built-in translation in-variance, since each convolution
kernel acts as it's own filter/feature detector.
Q4 What makes CNN’s translation-invariant?
As explained above, each convolution kernel acts as it's own filter/feature detector. So let's say
you're doing object detection, it doesn't matter where in the image the object is since we're going
to apply the convolution in a sliding window fashion across the entire image anyways.
Q5 Why do we have max-pooling in classification CNNs?
for a role in Computer Vision. Max-pooling in a CNN allows you to reduce computation since your
feature maps are smaller after the pooling. You don't lose too much semantic information since
you're taking the maximum activation. There's also a theory that max-pooling contributes a bit to
giving CNN’s more translation in-variance. Check out this great video from Andrew Ng on the
benefits of max-pooling.
Q6 Why do segmentation CNN’s typically have an encoder-decoder
style/structure?
The encoder CNN can basically be thought of as a feature extraction network, while the decoder
uses that information to predict the image segments by "decoding" the features and upscaling to
the original image size.
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Q7 What is the significance of Residual Networks?
The main thing that residual connections did was allow for direct feature access from previous
layers. This makes information propagation throughout the network much easier. One very
interesting paper about this shows how using local skip connections gives the network a type of
ensemble multi-path structure, giving features multiple paths to propagate throughout the
network.
Q8 What is batch normalization and why does it work?
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's
inputs changes during training, as the parameters of the previous layers change. The idea is then
to normalize the inputs of each layer in such a way that they have a mean output activation of
zero and a standard deviation of one. This is done for each individual mini-batch at each layer i.e
compute the mean and variance of that mini-batch alone, then normalize. This is analogous to
how the inputs to networks are standardized. How does this help? We know that normalizing the
inputs to a network helps it learn. But a network is just a series of layers, where the output of one
layer becomes the input to the next. That means we can think of any layer in a neural network as
the first layer of a smaller subsequent network. Thought of as a series of neural networks feeding
into each other, we normalize the output of one layer before applying the activation function and
then feed it into the following layer (sub-network).
Q9 Why would you use many small convolutional kernels such as 3x3 rather than
a few large ones?
This is very well explained in the VGGNet paper. There are 2 reasons: First, you can use several
smaller kernels rather than few large ones to get the same receptive field and capture more spatial
context, but with the smaller kernels you are using less parameters and computations. Secondly,
because with smaller kernels you will be using more filters, you'll be able to use more activation
functions and thus have a more discriminative mapping function being learned by your CNN.
Q10 What is Precision?
Precision (also called positive predictive value) is the fraction of relevant instances among the
retrieved instances
Precision = true positive / (true positive + false positive)
Q11 What is Recall?
Recall (also known as sensitivity) is the fraction of relevant instances that have been retrieved
over the total amount of relevant instances. Recall = true positive / (true positive + false negative)
Q12 Define F1-score.
It is the weighted average of precision and recall. It considers both false positive and false
negatives into account. It is used to measure the model’s performance.
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F1-Score = 2 * (precision * recall) / (precision + recall)
Q13 What is cost function?
The cost function is a scalar function that Quantifies the error factor of the Neural Network. Lower
the cost function better than the Neural network. Eg: MNIST Data set to classify the image, the
input image is digit 2 and the Neural network wrongly predicts it to be 3
Q14 List different activation neurons or functions
● Linear Neuron
● Binary Threshold Neuron
● Stochastic Binary Neuron
● Sigmoid Neuron
● Tanh function
● Rectified Linear Unit (ReLU)
Q15 Define Learning rate.
The learning rate is a hyper-parameter that controls how much we are adjusting the weights of
our network with respect to the loss gradient.
Q16 What is Momentum (w.r.t NN optimization)?
Momentum lets the optimization algorithm remembers its last step, and adds some proportion of
it to the current step. This way, even if the algorithm is stuck in a flat region, or a small local
minimum, it can get out and continue towards the true minimum.
Q17 What is the difference between Batch Gradient Descent and Stochastic
Gradient Descent?
Batch gradient descent computes the gradient using the whole dataset. This is great for convex
or relatively smooth error manifolds. In this case, we move somewhat directly towards an optimum
solution, either local or global. Additionally, batch gradient descent, given an annealed learning
rate, will eventually find the minimum located in its basin of attraction.
Stochastic gradient descent (SGD) computes the gradient using a single sample. SGD works well
(Not well, I suppose, but better than batch gradient descent) for error manifolds that have lots of
local maxima/minima. In this case, the somewhat noisier gradient calculated using the reduced
number of samples tends to jerk the model out of local minima into a region that hopefully is more
optimal.
Q18 Epoch vs Batch vs Iteration.
Epoch: one forward pass and one backward pass of all the training examples
Batch: examples processed together in one pass (forward and backward)
Iteration: number of training examples / Batch size
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Q19 What is the vanishing gradient?
As we add more and more hidden layers, backpropagation becomes less and less useful in
passing information to the lower layers. In effect, as information is passed back, the gradients
begin to vanish and become small relative to the weights of the networks.
Q20 What are dropouts?
Dropout is a simple way to prevent a neural network from overfitting. It is the dropping out of some
of the units in a neural network. It is similar to the natural reproduction process, where nature
produces offsprings by combining distinct genes (dropping out others) rather than strengthening
the co-adapting of them.
Q21 Can you explain the differences between supervised, unsupervised, and
reinforcement learning?
In supervised learning, we train a model to learn the relationship between input data and output
data. We need to have labeled data to be able to do supervised learning.
With unsupervised learning, we only have unlabeled data. The model learns a representation of
the data. Unsupervised learning is frequently used to initialize the parameters of the model when
we have a lot of unlabeled data and a small fraction of labeled data. We first train an unsupervised
model and, after that, we use the weights of the model to train a supervised model. In
reinforcement learning, the model has some input data and a reward depending on the output of
the model. The model learns a policy that maximizes the reward. Reinforcement learning has
been applied successfully to strategic games such as Go and even classic Atari video games.
Q22 What is data augmentation? Can you give some examples?
Data augmentation is a technique for synthesizing new data by modifying existing data in such a
way that the target is not changed, or it is changed in a known way. Computer vision is one of the
fields where data augmentation is very useful. There are many modifications that we can do to
images:
● Resize
● Horizontal or vertical flip
● Rotate, Add noise, Deform
● Modify colors Each problem needs a customized data augmentation pipeline. For
example, on OCR, doing flips will change the text and won’t be beneficial; however,
resizes and small rotations may help.
Q23 What are the components of GAN?
● Generator
● Discriminator
Q24 What’s the difference between a generative and discriminative model?
A generative model will learn categories of data while a discriminative model will simply learn the
distinction between different categories of data. Discriminative models will generally outperform
generative models on classification tasks.
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Q25 What is Linear Filtering?
Linear filtering is a neighborhood operation, which means that the output of a pixel’s value is
decided by the weighted sum of the values of the input pixels.
Q26 How can you achieve Blurring through Gaussian Filter?
This is the most common technique for blurring or smoothing an image. This filter improves the
resulting pixel found at the center and slowly minimizes the effects as pixels move away from the
center. This filter can also help in removing noise in an image
Q27 What is Non-Linear Filtering? How it is used?
Linear filtering is easy to use and implement. In some cases, this method is enough to get the
necessary output. However, an increase in performance can be obtained through non-linear
filtering. Through non-linear filtering, we can have more control and achieve better results when
we encounter a more complex computer vision task.
Q28 Explain Median Filtering.
The median filter is an example of a non-linear filtering technique. This technique is commonly
used for minimizing the noise in an image. It operates by inspecting the image pixel by pixel and
taking the place of each pixel’s value with the value of the neighboring pixel median.
Some techniques in detecting and matching features are:
● Lucas-Kanade
● Harris
● Shi-Tomasi
● SUSAN (smallest uni value segment assimilating nucleus)
● MSER (maximally stable extremal regions)
● SIFT (scale-invariant feature transform)
● HOG (histogram of oriented gradients)
● FAST (features from accelerated segment test)
● SURF (speeded-up robust features)
Q29 Describe the Scale Invariant Feature Transform (SIFT) algorithm
SIFT solves the problem of detecting the corners of an object even if it is scaled. Steps to
implement this algorithm:
● Scale-space extrema detection – This step will identify the locations and scales that can
still be recognized from different angles or views of the same object in an image.
● Keypoint localization – When possible key points are located, they would be refined to get
accurate results. This would result in the elimination of points that are low in contrast or
points that have edges that are deficiently localized.
● Orientation assignment – In this step, a consistent orientation is assigned to each key
point to attain invariance when the image is being rotated.
● Keypoint matching – In this step, the key points between images are now linked to
recognizing their nearest neighbors.
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Q30 Why Speeded-Up Robust Features (SURF) came into existence?
SURF was introduced to as a speed-up version of SIFT. Though SIFT can detect and describe
key points of an object in an image, still this algorithm is slow.
Q31 What is Oriented FAST and rotated BRIEF (ORB)?
This algorithm is a great possible substitute for SIFT and SURF, mainly because it performs better
in computation and matching. It is a combination of fast keypoint detector and brief descriptor,
which contains a lot of alterations to improve performance. It is also a great alternative in terms
of cost because the SIFT and SURF algorithms are patented, which means that you need to buy
them for their utilization.
Q32 What is image segmentation?
In computer vision, segmentation is the process of extracting pixels in an image that is related.
Segmentation algorithms usually take an image and produce a group of contours (the boundary
of an object that has well-defined edges in an image) or a mask where a set of related pixels are
assigned to a unique color value to identify it.
Popular image segmentation techniques:
● Active contours
● Level sets
● Graph-based merging
● Mean Shift
● Texture and intervening contour-based normalized cuts
Q33 What is the purpose of semantic segmentation?
The purpose of semantic segmentation is to categorize every pixel of an image to a certain class
or label. In semantic segmentation, we can see what is the class of a pixel by simply looking
directly at the color, but one downside of this is that we cannot identify if two colored masks belong
to a certain object.
Q34 Explain instance segmentation.
In semantic segmentation, the only thing that matters to us is the class of each pixel. This would
somehow lead to a problem that we cannot identify if that class belongs to the same object or not.
Semantic segmentation cannot identify if two objects in an image are separate entities. So to
solve this problem, instance segmentation was created. This segmentation can identify two
different objects of the same class. For example, if an image has two sheep in it, the sheep will
be detected and masked with different colors to differentiate what instance of a class they belong
to.
Q35 How is panoptic segmentation different from semantic/instance
segmentation?
Panoptic segmentation is basically a union of semantic and instance segmentation. In panoptic
segmentation, every pixel is classified by a certain class and those pixels that have several
instances of a class are also determined. For example, if an image has two cars, these cars will
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be masked with different colors. These colors represent the same class — car — but point to
different instances of a certain class.
Q36 Explain the problem of recognition in computer vision.
Recognition is one of the toughest challenges in the concepts in computer vision. Why is
recognition hard? For the human eyes, recognizing an object’s features or attributes would be
very easy. Humans can recognize multiple objects with very small effort. However, this does not
apply to a machine. It would be very hard for a machine to recognize or detect an object because
these objects vary. They vary in terms of viewpoints, sizes, or scales. Though these things are
still challenges faced by most computer vision systems, they are still making advancements or
approaches for solving these daunting tasks.
Q37 What is Object Recognition?
Object recognition is used for indicating an object in an image or video. This is a product of
machine learning and deep learning algorithms. Object recognition tries to acquire this innate
human ability, which is to understand certain features or visual detail of an image.
Q38 What is Object Detection and it’s real-life use cases?
Object detection in computer vision refers to the ability of machines to pinpoint the location of an
object in an image or video. A lot of companies have been using object detection techniques in
their system. They use it for face detection, web images, and security purposes.
Q39 Describe Optical Flow, its uses, and assumptions.
Optical flow is the pattern of apparent motion of image objects between two consecutive frames
caused by the movement of object or camera. It is a 2D vector field where each vector is a
displacement vector showing the movement of points from the first frame to the second
Optical flow has many applications in areas like :
● Structure from Motion
● Video Compression
● Video Stabilization …
Optical flow works on several assumptions:
1. The pixel intensities of an object do not change between consecutive frames.
2. Neighboring pixels have similar motion.
Q40 What is Histogram of Oriented Gradients (HOG)?
HOG stands for Histograms of Oriented Gradients. HOG is a type of “feature descriptor”. The
intent of a feature descriptor is to generalize the object in such a way that the same object (in this
case a person) produces as close as possible to the same feature descriptor when viewed under
different conditions. This makes the classification task easier.
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Q41 What is BOV: Bag-of-visual-words (BOV)?
BOV also called the bag of keypoints, is based on vector quantization. Similar to HOG features,
BOV features are histograms that count the number of occurrences of certain patterns within a
patch of the image.
Q42 What is Poselets? Where are poselets used?
Poselets rely on manually added extra keypoints such as “right shoulder”, “left shoulder”, “right
knee” and “left knee”. They were originally used for human pose estimation
Q43 Explain Textons in context of CNNs
A texton is the minimal building block of vision. The computer vision literature does not give a
strict definition for textons, but edge detectors could be one example. One might argue that deep
learning techniques with Convolution Neuronal Networks (CNNs) learn textons in the first filters.
Q44 What is Markov Random Fields (MRFs)?
MRFs are undirected probabilistic graphical models which are a wide-spread model in computer
vision. The overall idea of MRFs is to assign a random variable for each feature and a random
variable for each pixel
Q45 Explain the concept of superpixel?
A superpixel is an image patch that is better aligned with intensity edges than a rectangular patch.
Superpixels can be extracted with any segmentation algorithm, however, most of them produce
highly irregular superpixels, with widely varying sizes and shapes. A more regular space
tessellation may be desired.
Q46 What is Non-maximum suppression(NMS) and where is it used?
NMS is often used along with edge detection algorithms. The image is scanned along the image
gradient direction, and if pixels are not part of the local maxima they are set to zero. It is widely
used in object detection algorithms.
Q47 Describe the use of Computer Vision in Healthcare.
Computer vision has also been an important part of advances in health-tech. Computer vision
algorithms can help automate tasks such as detecting cancerous moles in skin images or finding
symptoms in x-ray and MRI scans.
Q48 Describe the use of Computer Vision in Augmented Reality & Mixed Reality
Computer vision also plays an important role in augmented and mixed reality, the technology that
enables computing devices such as smartphones, tablets, and smart glasses to overlay and
embed virtual objects on real-world imagery. Using computer vision, AR gear detects objects in
the real world in order to determine the locations on a device’s display to place a virtual object.
For instance, computer vision algorithms can help AR applications detect planes such as
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tabletops, walls, and floors, a very important part of establishing depth and dimensions and
placing virtual objects in the physical world.
Q49 Describe the use of Computer Vision in Facial Recognition
Computer vision also plays an important role in facial recognition applications, the technology that
enables computers to match images of people’s faces to their identities. Computer vision
algorithms detect facial features in images and compare them with databases of face profiles.
Consumer devices use facial recognition to authenticate the identities of their owners. Social
media apps use facial recognition to detect and tag users. Law enforcement agencies also rely
on facial recognition technology to identify criminals in video feeds.
Q50 Describe the use of Computer Vision in Self-Driving Cars
Computer vision enables self-driving cars to make sense of their surroundings. Cameras capture
video from different angles around the car and feed it to computer vision software, which then
processes the images in real-time to find the extremities of roads, read traffic signs, detect other
cars, objects, and pedestrians. The self-driving car can then steer its way on streets and
highways, avoid hitting obstacles, and (hopefully) safely drive its passengers to their destination.
Q51 Explain famous Computer Vision tasks using a single image example.
Many popular computer vision applications involve trying to recognize things in photographs; for
example:
Object Classification: What broad category of object is in this photograph?
Object Identification: Which type of a given object is in this photograph?
Object Verification: Is the object in the photograph?
Object Detection: Where are the objects in the photograph?
Object Landmark Detection: What are the key points for the object in the photograph?
Object Segmentation: What pixels belong to the object in the image?
Object Recognition: What objects are in this photograph and where are they?
Q52 Explain the distinction between Computer Vision and Image Processing.
Computer vision is distinct from image processing.
Image processing is the process of creating a new image from an existing image, typically
simplifying or enhancing the content in some way. It is a type of digital signal processing and is
not concerned with understanding the content of an image.
A given computer vision system may require image processing to be applied to raw input, e.g.
pre-processing images.
Examples of image processing include:
● Normalizing photometric properties of the image, such as brightness or color.
● Cropping the bounds of the image, such as centering an object in a photograph.
● Removing digital noise from an image, such as digital artifacts from low light levels.
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Q53 Explain business use cases in computer vision.
● Optical character recognition (OCR)
● Machine inspection
● Retail (e.g. automated checkouts)
● 3D model building (photogrammetry)
● Medical imaging
● Automotive safety
● Match move (e.g. merging CGI with live actors in movies)
● Motion capture (mocap)
● Surveillance
● Fingerprint recognition and biometrics
Q54 What is the Boltzmann Machine?
One of the most basic Deep Learning models is a Boltzmann Machine, resembling a simplified
version of the Multi-Layer Perceptron. This model features a visible input layer and a hidden layer
-- just a two-layer neural net that makes stochastic decisions as to whether a neuron should be
on or off. Nodes are connected across layers, but no two nodes of the same layer are connected.
Q56 What Is the Role of Activation Functions in a Neural Network?
At the most basic level, an activation function decides whether a neuron should be fired or not. It
accepts the weighted sum of the inputs and bias as input to any activation function. Step function,
Sigmoid, ReLU, Tanh, and Softmax are examples of activation functions.
Q57 What Is the Difference Between a Feedforward Neural Network and Recurrent
Neural Network?
A Feedforward Neural Network signals travel in one direction from input to output. There are no
feedback loops; the network considers only the current input. It cannot memorize previous inputs
(e.g., CNN).
Q58 What Are the Applications of a Recurrent Neural Network (RNN)?
The RNN can be used for sentiment analysis, text mining, and image captioning. Recurrent Neural
Networks can also address time series problems such as predicting the prices of stocks in a
month or quarter.
Q59 What Are the Softmax and ReLU Functions?
Softmax is an activation function that generates the output between zero and one. It divides each
output, such that the total sum of the outputs is equal to one. Softmax is often used for output
layers.
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Q60 What Are Hyperparameters?
With neural networks, you’re usually working with hyperparameters once the data is formatted
correctly. A hyperparameter is a parameter whose value is set before the learning process begins.
It determines how a network is trained and the structure of the network (such as the number of
hidden units, the learning rate, epochs, etc.).
Q61 What Will Happen If the Learning Rate Is Set Too Low or Too High?
When your learning rate is too low, training of the model will progress very slowly as we are
making minimal updates to the weights. It will take many updates before reaching the minimum
point. If the learning rate is set too high, this causes undesirable divergent behavior to the loss
function due to drastic updates in weights. It may fail to converge (model can give a good output)
or even diverge (data is too chaotic for the network to train).
Q62 How Are Weights Initialized in a Network?
There are two methods here: we can either initialize the weights to zero or assign them randomly.
Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and
every layer perform the same operation, giving the same output and making the deep net useless.
Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very
close to 0. It gives better accuracy to the model since every neuron performs different
computations. This is the most commonly used method.
Q63 What Are the Different Layers on CNN?
There are four layers in CNN:
1. Convolutional Layer - the layer that performs a convolutional operation, creating several
smaller picture windows to go over the data.
2. ReLU Layer - it brings non-linearity to the network and converts all the negative pixels to
zero. The output is a rectified feature map.
3. Pooling Layer - pooling is a down-sampling operation that reduces the dimensionality of
the feature map.
4. Fully Connected Layer - this layer recognizes and classifies the objects in the image.
Q64 What is Pooling on CNN, and How Does It Work?
Pooling is used to reduce the spatial dimensions of a CNN. It performs down-sampling operations
to reduce the dimensionality and creates a pooled feature map by sliding a filter matrix over the
input matrix.
Q65 How Does an LSTM Network Work?
Long-Short-Term Memory (LSTM) is a special kind of recurrent neural network capable of learning
long-term dependencies, remembering information for long periods as its default behavior. There
are three steps in an LSTM network:
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● Step 1: The network decides what to forget and what to remember.
● Step 2: It selectively updates cell state values.
● Step 3: The network decides what part of the current state makes it to the output.
Q66 What Is the Difference Between Epoch, Batch, and Iteration in Deep Learning?
● Epoch - Represents one iteration over the entire dataset (everything put into the training
model).
● Batch - Refers to when we cannot pass the entire dataset into the neural network at once,
so we divide the dataset into several batches.
● Iteration - if we have 10,000 images as data and a batch size of 200. then an epoch should
run 50 iterations (10,000 divided by 50).
Q67 Why Is Tensorflow the Most Preferred Library in Deep Learning?
Tensorflow provides both C++ and Python APIs, making it easier to work on and has a faster
compilation time compared to other Deep Learning libraries like Keras and Torch. Tensorflow
supports both CPU and GPU computing devices.
Q68 What Do You Mean by Tensor in Tensorflow?
A tensor is a mathematical object represented as arrays of higher dimensions. These arrays of
data with different dimensions and ranks fed as input to the neural network are called “Tensors.”
Q69 Explain a Computational Graph.
Everything in TensorFlow is based on creating a computational graph. It has a network of nodes
where each node operates, Nodes represent mathematical operations, and edges represent
tensors. Since data flows in the form of a graph, it is also called a “DataFlow Graph.”
Q70 What Is an Auto-encoder?
This Neural Network has three layers in which the input neurons are equal to the output neurons.
The network's target outside is the same as the input. It uses dimensionality reduction to
restructure the input. It works by compressing the image input to a latent space representation
then reconstructing the output from this representation.
Q71 Can we have the same bias for all neurons of a hidden layer?
Essentially, you can have a different bias value at each layer or at each neuron as well. However,
it is best if we have a bias matrix for all the neurons in the hidden layers as well.
A point to note is that both these strategies would give you very different results.
Q72 In a neural network, what if all the weights are initialized with the same value?
In simplest terms, if all the neurons have the same value of weights, each hidden unit will get
exactly the same signal. While this might work during forward propagation, the derivative of the
cost function during backward propagation would be the same every time.
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In short, there is no learning happening by the network! What do you call the phenomenon of the
model being unable to learn any patterns from the data? Yes, underfitting.
Therefore, if all weights have the same initial value, this would lead to underfitting.
Q73 What is the role of weights and bias in a neural network?
This is a question best explained with a real-life example. Consider that you want to go out today
to play a cricket match with your friends. Now, a number of factors can affect your decision-
making, like:
● How many of your friends can make it to the game?
● How much equipment can all of you bring?
● What is the temperature outside?
And so on. These factors can change your decision greatly or not too much. For example, if it is
raining outside, then you cannot go out to play at all. Or if you have only one bat, you can share
it while playing as well. The magnitude by which these factors can affect the game is called the
weight of that factor.
Factors like the weather or temperature might have a higher weight, and other factors like
equipment would have a lower weight.
Q74 Why does a Convolutional Neural Network (CNN) work better with image data?
The key to this question lies in the Convolution operation. Unlike humans, the machine sees the
image as a matrix of pixel values. Instead of interpreting a shape like a petal or an ear, it just
identifies curves and edges.
Thus, instead of looking at the entire image, it helps to just read the image in parts. Doing this for
a 300 x 300-pixel image would mean dividing the matrix into smaller 3 x 3 matrices and dealing
with them one by one. This is convolution.
Q75 Why do RNNs work better with text data?
The main component that differentiates Recurrent Neural Networks (RNN) from the other models
is the addition of a loop at each node. This loop brings the recurrence mechanism in RNNs. In a
basic Artificial Neural Network (ANN), each input is given the same weight and fed to the network
at the same time. So, for a sentence like “I saw the movie and hated it”, it would be difficult to
capture the information which associates “it” with the “movie”.
Q76 In a CNN, if the input size 5 X 5 and the filter size is 7 X 7, then what would be
the size of the output?
This is a pretty intuitive answer. As we saw above, we perform the convolution on ‘x’ one step at
a time, to the right, and in the end, we got Z with dimensions 2 X 2, for X with dimensions 3 X 3.
Thus, to make the input size similar to the filter size, we make use of padding – adding 0s to the
input matrix such that its new size becomes at least 7 X 7. Thus, the output size would be using
the formula:
Dimension of image = (n, n) = 5 X 5
Dimension of filter = (f,f) = 7 X 7
Padding = 1 (adding 1 pixel with value 0 all around the edges)
Dimension of output will be (n+2p-f+1) X (n+2p-f+1) = 1 X 1
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Q77 What’s the difference between valid and same padding in a CNN?
This question has more chances of being a follow-up question to the previous one. Or if you have
explained how you used CNNs in a computer vision task, the interviewer might ask this question
along with the details of the padding parameters.
● Valid Padding: When we do not use any padding. The resultant matrix after convolution
will have dimensions (n – f + 1) X (n – f + 1)
● Same padding: Adding padded elements all around the edges such that the output matrix
will have the same dimensions as that of the input matrix
Q78 What are the applications of transfer learning in Deep Learning?
I am sure you would have a doubt as to why a relatively simple question was included in the
Intermediate Level. The reason is the sheer volume of subsequent questions it can generate! The
use of transfer learning has been one of the key milestones in deep learning. Training a large
model on a huge dataset, and then using the final parameters on smaller simpler datasets has
led to defining breakthroughs in the form of Pretrained Models. Be it Computer Vision or NLP,
pretrained models have become the norm in research and in the industry. Some popular
examples include BERT, ResNet, GPT-2, VGG-16, etc, and many more.
Q79 Why is GRU faster as compared to LSTM?
As you can see, the LSTM model can become quite complex. In order to still retain the
functionality of retaining information across time and yet not make a too complex model, we need
GRUs. Basically, in GRUs, instead of having an additional Forget gate, we combine the input and
Forget gates into a single Update Gate:
Q80 How is the transformer architecture better than RNN?
Advancements in deep learning have made it possible to solve many tasks in Natural Language
Processing. Networks/Sequence models like RNNs, LSTMs, etc. are specifically used for this
purpose – so as to capture all possible information from a given sentence, or a paragraph.
However, sequential processing comes with its caveats:
● It requires high processing power
● It is difficult to execute in parallel because of its sequential nature
Q81 How Can We Scale GANs Beyond Image Synthesis?
Aside from applications like image-to-image translation and domain-adaptation most GAN
successes have been in image synthesis. Attempts to use GANs beyond images have focused
on three domains: Text, Structured Data and Audio
Q82 How Should we Evaluate GANs and When Should We Use Them?
When it comes to evaluating GANs, there are many proposals but little consensus. Suggestions
include:
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● Inception Score and FID - Both these scores use a pre-trained image classifier and both
have known issues. A common criticism is that these scores measure ‘sample quality’ and
don’t really capture ‘sample diversity’.
● MS-SSIM - propose using MS-SSIM to separately evaluate diversity, but this technique
has some issues and hasn’t really caught on.
● AIS - propose putting a Gaussian observation model on the outputs of a GAN and using
annealed importance sampling to estimate the log-likelihood under this model, but show
that estimates computed this way are inaccurate in the case where the GAN generator is
also a flow model The generator being a flow model allows for the computation of exact
log-likelihoods in this case.
● Geometry Score - suggest computing geometric properties of the generated data manifold
and comparing those properties to the real data.
● Precision and Recall - attempt to measure both the ‘precision’ and ‘recall’ of GANs.
● Skill Rating - have shown that trained GAN discriminators can contain useful information
with which evaluation can be performed.
Q83 What should we use GANs for?
If you want an actual density model, GANs probably isn’t the best choice. There is now good
experimental evidence that GANs learn a ‘low support’ representation of the target dataset, which
means there may be substantial parts of the test set to which a GAN (implicitly) assigns zero
likelihood.
Q84 How should we evaluate GANs on these perceptual tasks?
Ideally, we would just use a human judge, but this is expensive. A cheap proxy is to see if a
classifier can distinguish between real and fake examples. This is called a classifier two-sample
test (C2STs). The main issue with C2STs is that if the Generator has even a minor defect that’s
systematic across samples (e.g., ) this will dominate the evaluation.
Q85 Explain the problem of Vanishing Gradients in GANs
Research has suggested that if your discriminator is too good, then generator training can fail due
to vanishing gradients. In effect, an optimal discriminator doesn't provide enough information for
the generator to make progress.
Attempts to Remedy
● Wasserstein loss: The Wasserstein loss is designed to prevent vanishing gradients even
when you train the discriminator to optimality.
● Modified minimax loss: The original GAN paper proposed a modification to minimax loss
to deal with vanishing gradients.
Q86 What is Mode Collapse and why it is a big issue?
Usually, you want your GAN to produce a wide variety of outputs. You want, for example, a
different face for every random input to your face generator.
However, if a generator produces an especially plausible output, the generator may learn to
produce only that output. In fact, the generator is always trying to find the one output that seems
most plausible to the discriminator.
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If the generator starts producing the same output (or a small set of outputs) over and over again,
the discriminator's best strategy is to learn to always reject that output. But if the next generation
of discriminator gets stuck in a local minimum and doesn't find the best strategy, then it's too easy
for the next generator iteration to find the most plausible output for the current discriminator.
Each iteration of generator over-optimizes for a particular discriminator and the discriminator
never manages to learn its way out of the trap. As a result, the generators rotate through a small
set of output types. This form of GAN failure is called mode collapse.
Q87 ExplainProgressive GANs
In a progressive GAN, the generator's first layers produce very low resolution images, and
subsequent layers add details. This technique allows the GAN to train more quickly than
comparable non-progressive GANs, and produces higher resolution images.
Q88 Explain Conditional GANs
Conditional GANs train on a labeled data set and let you specify the label for each generated
instance. For example, an unconditional MNIST GAN would produce random digits, while a
conditional MNIST GAN would let you specify which digit the GAN should generate.
Instead of modeling the joint probability P(X, Y), conditional GANs model the conditional
probability P(X | Y).
For more information about conditional GANs, see Mirza et al, 2014.
Q89 Explain Image-to-Image Translation
Image-to-Image translation GANs take an image as input and map it to a generated output image
with different properties. For example, we can take a mask image with blob of color in the shape
of a car, and the GAN can fill in the shape with photorealistic car details.
Q90 Explain CycleGAN
CycleGANs learn to transform images from one set into images that could plausibly belong to
another set. For example, a CycleGAN produced the righthand image below when given the
lefthand image as input. It took an image of a horse and turned it into an image of a zebra.
Q91 What is Super-resolution?
Super-resolution GANs increase the resolution of images, adding detail where necessary to fill in
blurry areas. For example, the blurry middle image below is a downsampled version of the original
image on the left. Given the blurry image, a GAN produced the sharper image on the right:
Q92 Explain different problems in GANs
Many GAN models suffer the following major problems:
● Non-convergence: the model parameters oscillate, destabilize and never converge,
● Mode collapse: the generator collapses which produces limited varieties of samples,
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● Diminished gradient: the discriminator gets too successful that the generator gradient
vanishes and learns nothing,
● Unbalance between the generator and discriminator causing overfitting, &
● Highly sensitive to the hyperparameter selections.
Q93 Describe Cost v.s. image quality in GANS?
In a discriminative model, the loss measures the accuracy of the prediction and we use it to
monitor the progress of the training. However, the loss in GAN measures how well we are doing
compared with our opponent. Often, the generator cost increases but the image quality is actually
improving. We fall back to examine the generated images manually to verify the progress. This
makes model comparison harder which leads to difficulties in picking the best model in a single
run. It also complicates the tuning process.
Q94 Why Singular Value Decomposition (SVD) is used in Computer Vision?
The singular value decomposition is the most common and useful decomposition in computer
vision. The goal of computer vision is to explain the three-dimensional world through two-
dimensional pictures.
Q95 What Is Image Transform?
An image can be expanded in terms of a discrete set of basis arrays called basis images. Hence,
these basis images can be generated by unitary matrices. An NxN image can be viewed as an
N^2×1 vector. It provides a set of coordinates or basis vectors for vector space.
Q96 List The Hardware Oriented Color Models?
They are as follows.
– RGB model
– CMY model
– YIQ model
– HSI model
Q96 What Is The Need For Transform?
Answer: The need for transform is most of the signals or images are time-domain signal (ie)
signals can be measured with a function of time. This representation is not always best. Any
person of the mathematical transformations is applied to the signal or images to obtain further
information from that signal. Particularly, for image processing.
Q97 What is FPN?
Feature Pyramid Network (FPN) is a feature extractor designed with a feature pyramid concept
to improve accuracy and speed. Images are first to pass through the CNN pathway, yielding
semantically rich final layers. Then to regain better resolution, it creates a top-down pathway by
upsampling this feature map.
